https://twitter.com/SamueleCornell/status/1849115845516984758
https://arxiv.org/abs/2408.09215
Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition
Samuele Cornell, Jordan Darefsky, Zhiyao Duan, Shinji Watanabe
Currently, a common approach in many speech processing tasks is to leverage large scale pre-trained models by fine-tuning them on in-domain data for a particular application. Yet obtaining even a small amount of such data can be problematic, especially for sensitive domains and conversational speech scenarios, due to both privacy issues and annotation costs. To address this, synthetic data generation using single speaker datasets has been employed. Yet, for multi-speaker cases, such an approach often requires extensive manual effort and is prone to domain mismatches. In this work, we propose a synthetic data generation pipeline for multi-speaker conversational ASR, leveraging a large language model (LLM) for content creation and a conversational multi-speaker text-to-speech (TTS) model for speech synthesis. We conduct evaluation by fine-tuning the Whisper ASR model for telephone and distant conversational speech settings, using both in-domain data and generated synthetic data. Our results show that the proposed method is able to significantly outperform classical multi-speaker generation approaches that use external, non-conversational speech datasets.
https://arxiv.org/abs/2408.09215
Generating Data with Text-to-Speech and Large-Language Models for Conversational Speech Recognition
Samuele Cornell, Jordan Darefsky, Zhiyao Duan, Shinji Watanabe
Currently, a common approach in many speech processing tasks is to leverage large scale pre-trained models by fine-tuning them on in-domain data for a particular application. Yet obtaining even a small amount of such data can be problematic, especially for sensitive domains and conversational speech scenarios, due to both privacy issues and annotation costs. To address this, synthetic data generation using single speaker datasets has been employed. Yet, for multi-speaker cases, such an approach often requires extensive manual effort and is prone to domain mismatches. In this work, we propose a synthetic data generation pipeline for multi-speaker conversational ASR, leveraging a large language model (LLM) for content creation and a conversational multi-speaker text-to-speech (TTS) model for speech synthesis. We conduct evaluation by fine-tuning the Whisper ASR model for telephone and distant conversational speech settings, using both in-domain data and generated synthetic data. Our results show that the proposed method is able to significantly outperform classical multi-speaker generation approaches that use external, non-conversational speech datasets.
X (formerly Twitter)
Samuele Cornell (@SamueleCornell) on X
In this paper we show that is possible to create synthetic 2-speakers conversations with TTS and LLMs and fine-tune successfully Whisper for multi-speaker ASR generalizing well to real-world scenarios:
https://t.co/Z4b4YrLzpR
Examples of such synth data:…
https://t.co/Z4b4YrLzpR
Examples of such synth data:…
Speech Technology
"We don't want 200ms latency, that's just not useful" Will Williams is CTO of Speechmatics in Cambridge. In this sponsored episode - he shares deep technical insights into modern speech recognition technology and system architecture. The episode covers several…
Some notes on Speechmatics interview:
Latency should be dynamic, modern advertising about small latency is not reasonable, but dynamic context-dependent latency is a thing. AudioLLMs enable that.
Lattices are not the optimal way of representation of the search space if you have may aspects of speech (emotion, etc). Vectorized representations suit GPU better, more compact and learnable. By using lattices we have some control over results but restrict ourselves at the same time.
Wav2vec-like learning Speechmatics uses is 100x faster but at the same time it is very hard to learn long distribution tail without lexical information just from the audio. Semi-supervised learning or full e2e approach definitely have an advantage.
Continuous learning (active inference) is something to think about more actively, yes, something very important for the future.
Latency should be dynamic, modern advertising about small latency is not reasonable, but dynamic context-dependent latency is a thing. AudioLLMs enable that.
Lattices are not the optimal way of representation of the search space if you have may aspects of speech (emotion, etc). Vectorized representations suit GPU better, more compact and learnable. By using lattices we have some control over results but restrict ourselves at the same time.
Wav2vec-like learning Speechmatics uses is 100x faster but at the same time it is very hard to learn long distribution tail without lexical information just from the audio. Semi-supervised learning or full e2e approach definitely have an advantage.
Continuous learning (active inference) is something to think about more actively, yes, something very important for the future.
We released new Vosk models for Persian, WER improved significantly
https://alphacephei.com/vosk/models/vosk-model-fa-0.42.zip
https://alphacephei.com/vosk/models/vosk-model-small-fa-0.42.zip
For more details see
https://github.com/alphacep/awesome-speech/blob/main/persian.md#asr-results
https://alphacephei.com/vosk/models/vosk-model-fa-0.42.zip
https://alphacephei.com/vosk/models/vosk-model-small-fa-0.42.zip
For more details see
https://github.com/alphacep/awesome-speech/blob/main/persian.md#asr-results
https://arxiv.org/abs/2410.18908
A Survey on Speech Large Language Models
Jing Peng, Yucheng Wang, Yu Xi, Xu Li, Xizhuo Zhang, Kai Yu
Large Language Models (LLMs) exhibit strong contextual understanding and remarkable multi-task performance. Therefore, researchers have been seeking to integrate LLMs in the broad sense of Spoken Language Understanding (SLU) field. Different from the traditional method of cascading LLMs to process text generated by Automatic Speech Recognition(ASR), new efforts have focused on designing architectures centered around Audio Feature Extraction - Multimodal Information Fusion - LLM Inference(Speech LLMs). This approach enables richer audio feature extraction while simultaneously facilitating end-to-end fusion of audio and text modalities, thereby achieving deeper understanding and reasoning from audio data. This paper elucidates the development of Speech LLMs, offering an in-depth analysis of system architectures and training strategies. Through extensive research and a series of targeted experiments, the paper assesses Speech LLMs' advancements in Rich Audio Transcription and its potential for Cross-task Integration within the SLU field. Additionally, it indicates key challenges uncovered through experimentation, such as the Dormancy of LLMs under certain conditions. The paper further delves into the training strategies for Speech LLMs, proposing potential solutions based on these findings, and offering valuable insights and references for future research in this domain, as well as LLM applications in multimodal contexts.
A Survey on Speech Large Language Models
Jing Peng, Yucheng Wang, Yu Xi, Xu Li, Xizhuo Zhang, Kai Yu
Large Language Models (LLMs) exhibit strong contextual understanding and remarkable multi-task performance. Therefore, researchers have been seeking to integrate LLMs in the broad sense of Spoken Language Understanding (SLU) field. Different from the traditional method of cascading LLMs to process text generated by Automatic Speech Recognition(ASR), new efforts have focused on designing architectures centered around Audio Feature Extraction - Multimodal Information Fusion - LLM Inference(Speech LLMs). This approach enables richer audio feature extraction while simultaneously facilitating end-to-end fusion of audio and text modalities, thereby achieving deeper understanding and reasoning from audio data. This paper elucidates the development of Speech LLMs, offering an in-depth analysis of system architectures and training strategies. Through extensive research and a series of targeted experiments, the paper assesses Speech LLMs' advancements in Rich Audio Transcription and its potential for Cross-task Integration within the SLU field. Additionally, it indicates key challenges uncovered through experimentation, such as the Dormancy of LLMs under certain conditions. The paper further delves into the training strategies for Speech LLMs, proposing potential solutions based on these findings, and offering valuable insights and references for future research in this domain, as well as LLM applications in multimodal contexts.
arXiv.org
A Survey on Speech Large Language Models for Understanding
Speech understanding is essential for interpreting the diverse forms of information embedded in spoken language, including linguistic, paralinguistic, and non-linguistic cues that are vital for...
Nice paper with few interesting details. Extra CTC head for Whisper stabilization is interesting for example.
https://arxiv.org/abs/2409.09543
Target Speaker ASR with Whisper
Alexander Polok, Dominik Klement, Matthew Wiesner, Sanjeev Khudanpur, Jan Černocký, Lukáš Burget
We propose a novel approach to enable the use of large, single speaker ASR models, such as Whisper, for target speaker ASR. The key insight of this method is that it is much easier to model relative differences among speakers by learning to condition on frame-level diarization outputs, than to learn the space of all speaker embeddings. We find that adding even a single bias term per diarization output type before the first transformer block can transform single speaker ASR models, into target speaker ASR models. Our target-speaker ASR model can be used for speaker attributed ASR by producing, in sequence, a transcript for each hypothesized speaker in a diarization output. This simplified model for speaker attributed ASR using only a single microphone outperforms cascades of speech separation and diarization by 11% absolute ORC-WER on the NOTSOFAR-1 dataset.
https://arxiv.org/abs/2409.09543
Target Speaker ASR with Whisper
Alexander Polok, Dominik Klement, Matthew Wiesner, Sanjeev Khudanpur, Jan Černocký, Lukáš Burget
We propose a novel approach to enable the use of large, single speaker ASR models, such as Whisper, for target speaker ASR. The key insight of this method is that it is much easier to model relative differences among speakers by learning to condition on frame-level diarization outputs, than to learn the space of all speaker embeddings. We find that adding even a single bias term per diarization output type before the first transformer block can transform single speaker ASR models, into target speaker ASR models. Our target-speaker ASR model can be used for speaker attributed ASR by producing, in sequence, a transcript for each hypothesized speaker in a diarization output. This simplified model for speaker attributed ASR using only a single microphone outperforms cascades of speech separation and diarization by 11% absolute ORC-WER on the NOTSOFAR-1 dataset.
arXiv.org
Target Speaker ASR with Whisper
We propose a novel approach to enable the use of large, single-speaker ASR models, such as Whisper, for target speaker ASR. The key claim of this method is that it is much easier to model relative...
Even with our new speech codec, producing a 2-minute dialogue requires generating over 5000 tokens. To model these long sequences, we developed a specialized Transformer architecture that can efficiently handle hierarchies of information, matching the structure of our acoustic tokens.
https://deepmind.google/discover/blog/pushing-the-frontiers-of-audio-generation/
https://deepmind.google/discover/blog/pushing-the-frontiers-of-audio-generation/
Google DeepMind
Pushing the frontiers of audio generation
Our pioneering speech generation technologies are helping people around the world interact with more natural, conversational and intuitive digital assistants and AI tools.
Fish Agent V0.1 3B is a groundbreaking Voice-to-Voice model capable of capturing and generating environmental audio information with unprecedented accuracy. What sets it apart is its semantic-token-free architecture, eliminating the need for traditional semantic encoders/decoders like Whisper and CosyVoice.
Additionally, it stands as a state-of-the-art text-to-speech (TTS) model, trained on an extensive dataset of 700,000 hours of multilingual audio content.
This model is a continue-pretrained version of Qwen-2.5-3B-Instruct for 200B voice & text tokens.
https://huggingface.co/fishaudio/fish-agent-v0.1-3b
Additionally, it stands as a state-of-the-art text-to-speech (TTS) model, trained on an extensive dataset of 700,000 hours of multilingual audio content.
This model is a continue-pretrained version of Qwen-2.5-3B-Instruct for 200B voice & text tokens.
https://huggingface.co/fishaudio/fish-agent-v0.1-3b
huggingface.co
fishaudio/fish-agent-v0.1-3b · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Overall, we find no evidence that multiscale aspects of MR-HuBERT lead to improved acquisition of high level concepts. The question now is how to build an architecture that does leverage this hierarchy?🤔 (4/5)
https://twitter.com/theo_clark_/status/1852299593272131874
https://arxiv.org/abs/2410.23955
https://twitter.com/theo_clark_/status/1852299593272131874
https://arxiv.org/abs/2410.23955
X (formerly Twitter)
Theo Clark (@theo_clark_) on X
Overall, we find no evidence that multiscale aspects of MR-HuBERT lead to improved acquisition of high level concepts. The question now is how to build an architecture that does leverage this hierarchy?🤔 (4/5)
It is simply bad
https://arxiv.org/abs/2411.03866
Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward
Shashi Kumar, Iuliia Thorbecke, Sergio Burdisso, Esaú Villatoro-Tello, Manjunath K E, Kadri Hacioğlu, Pradeep Rangappa, Petr Motlicek, Aravind Ganapathiraju, Andreas Stolcke
Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whether these simple approaches are robust enough across different scenarios and speech conditions, such as domain shifts and different speech perturbations. In this paper, we address these questions by conducting various ablation experiments using a recent and widely adopted approach called SLAM-ASR. We present novel empirical findings that offer insights on how to effectively utilize the SLAM-ASR architecture across a wide range of settings. Our main findings indicate that the SLAM-ASR exhibits poor performance in cross-domain evaluation settings. Additionally, speech perturbations within in-domain data, such as changes in speed or the presence of additive noise, can significantly impact performance. Our findings offer critical insights for fine-tuning and configuring robust LLM-based ASR models, tailored to different data characteristics and computational resources.
https://arxiv.org/abs/2411.03866
Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward
Shashi Kumar, Iuliia Thorbecke, Sergio Burdisso, Esaú Villatoro-Tello, Manjunath K E, Kadri Hacioğlu, Pradeep Rangappa, Petr Motlicek, Aravind Ganapathiraju, Andreas Stolcke
Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whether these simple approaches are robust enough across different scenarios and speech conditions, such as domain shifts and different speech perturbations. In this paper, we address these questions by conducting various ablation experiments using a recent and widely adopted approach called SLAM-ASR. We present novel empirical findings that offer insights on how to effectively utilize the SLAM-ASR architecture across a wide range of settings. Our main findings indicate that the SLAM-ASR exhibits poor performance in cross-domain evaluation settings. Additionally, speech perturbations within in-domain data, such as changes in speed or the presence of additive noise, can significantly impact performance. Our findings offer critical insights for fine-tuning and configuring robust LLM-based ASR models, tailored to different data characteristics and computational resources.
arXiv.org
Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly,...
Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities....
Apple's papers are always very practical. This one is also good, many in-depth experiments and practical cases. Note that biasing effect is minimal (usually WER goes down a little 17% -> 15%).
https://arxiv.org/abs/2411.00664
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval
Nikolaos Flemotomos, Roger Hsiao, Pawel Swietojanski, Takaaki Hori, Dogan Can, Xiaodan Zhuang
Neural contextual biasing allows speech recognition models to leverage contextually relevant information, leading to improved transcription accuracy. However, the biasing mechanism is typically based on a cross-attention module between the audio and a catalogue of biasing entries, which means computational complexity can pose severe practical limitations on the size of the biasing catalogue and consequently on accuracy improvements. This work proposes an approximation to cross-attention scoring based on vector quantization and enables compute- and memory-efficient use of large biasing catalogues. We propose to use this technique jointly with a retrieval based contextual biasing approach. First, we use an efficient quantized retrieval module to shortlist biasing entries by grounding them on audio. Then we use retrieved entries for biasing. Since the proposed approach is agnostic to the biasing method, we investigate using full cross-attention, LLM prompting, and a combination of the two. We show that retrieval based shortlisting allows the system to efficiently leverage biasing catalogues of several thousands of entries, resulting in up to 71% relative error rate reduction in personal entity recognition. At the same time, the proposed approximation algorithm reduces compute time by 20% and memory usage by 85-95%, for lists of up to one million entries, when compared to standard dot-product cross-attention.
https://arxiv.org/abs/2411.00664
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval
Nikolaos Flemotomos, Roger Hsiao, Pawel Swietojanski, Takaaki Hori, Dogan Can, Xiaodan Zhuang
Neural contextual biasing allows speech recognition models to leverage contextually relevant information, leading to improved transcription accuracy. However, the biasing mechanism is typically based on a cross-attention module between the audio and a catalogue of biasing entries, which means computational complexity can pose severe practical limitations on the size of the biasing catalogue and consequently on accuracy improvements. This work proposes an approximation to cross-attention scoring based on vector quantization and enables compute- and memory-efficient use of large biasing catalogues. We propose to use this technique jointly with a retrieval based contextual biasing approach. First, we use an efficient quantized retrieval module to shortlist biasing entries by grounding them on audio. Then we use retrieved entries for biasing. Since the proposed approach is agnostic to the biasing method, we investigate using full cross-attention, LLM prompting, and a combination of the two. We show that retrieval based shortlisting allows the system to efficiently leverage biasing catalogues of several thousands of entries, resulting in up to 71% relative error rate reduction in personal entity recognition. At the same time, the proposed approximation algorithm reduces compute time by 20% and memory usage by 85-95%, for lists of up to one million entries, when compared to standard dot-product cross-attention.
arXiv.org
Optimizing Contextual Speech Recognition Using Vector Quantization...
Neural contextual biasing allows speech recognition models to leverage contextually relevant information, leading to improved transcription accuracy. However, the biasing mechanism is typically...
SANE 2024 Videos, interesting things
https://www.youtube.com/playlist?list=PLBJWRPcgwk7vVzKLPnTrqm831VohoLMmy
https://www.youtube.com/playlist?list=PLBJWRPcgwk7vVzKLPnTrqm831VohoLMmy
YouTube
SANE 2024 @ Google Cambridge
SANE 2024, a one-day event gathering researchers and students in speech and audio from the Northeast of the American continent, was held on Thursday October ...
https://github.com/jishengpeng/WavChat
https://arxiv.org/abs/2411.13577
WavChat: A Survey of Spoken Dialogue Models
Shengpeng Ji, Yifu Chen, Minghui Fang, Jialong Zuo, Jingyu Lu, Hanting Wang, Ziyue Jiang, Long Zhou, Shujie Liu, Xize Cheng, Xiaoda Yang, Zehan Wang, Qian Yang, Jian Li, Yidi Jiang, Jingzhen He, Yunfei Chu, Jin Xu, Zhou Zhao
Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. Compared to traditional three-tier cascaded spoken dialogue models that comprise speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS), modern spoken dialogue models exhibit greater intelligence. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Moreover, they generate high-quality, multi-turn speech responses with low latency, enabling real-time interaction through simultaneous listening and speaking capability. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems and the underlying technologies. To address this, we have first compiled existing spoken dialogue systems in the chronological order and categorized them into the cascaded and end-to-end paradigms. We then provide an in-depth overview of the core technologies in spoken dialogue models, covering aspects such as speech representation, training paradigm, streaming, duplex, and interaction capabilities. Each section discusses the limitations of these technologies and outlines considerations for future research. Additionally, we present a thorough review of relevant datasets, evaluation metrics, and benchmarks from the perspectives of training and evaluating spoken dialogue systems. We hope this survey will contribute to advancing both academic research and industrial applications in the field of spoken dialogue systems. The related material is available at this https URL.
https://arxiv.org/abs/2411.13577
WavChat: A Survey of Spoken Dialogue Models
Shengpeng Ji, Yifu Chen, Minghui Fang, Jialong Zuo, Jingyu Lu, Hanting Wang, Ziyue Jiang, Long Zhou, Shujie Liu, Xize Cheng, Xiaoda Yang, Zehan Wang, Qian Yang, Jian Li, Yidi Jiang, Jingzhen He, Yunfei Chu, Jin Xu, Zhou Zhao
Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. Compared to traditional three-tier cascaded spoken dialogue models that comprise speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS), modern spoken dialogue models exhibit greater intelligence. These advanced spoken dialogue models not only comprehend audio, music, and other speech-related features, but also capture stylistic and timbral characteristics in speech. Moreover, they generate high-quality, multi-turn speech responses with low latency, enabling real-time interaction through simultaneous listening and speaking capability. Despite the progress in spoken dialogue systems, there is a lack of comprehensive surveys that systematically organize and analyze these systems and the underlying technologies. To address this, we have first compiled existing spoken dialogue systems in the chronological order and categorized them into the cascaded and end-to-end paradigms. We then provide an in-depth overview of the core technologies in spoken dialogue models, covering aspects such as speech representation, training paradigm, streaming, duplex, and interaction capabilities. Each section discusses the limitations of these technologies and outlines considerations for future research. Additionally, we present a thorough review of relevant datasets, evaluation metrics, and benchmarks from the perspectives of training and evaluating spoken dialogue systems. We hope this survey will contribute to advancing both academic research and industrial applications in the field of spoken dialogue systems. The related material is available at this https URL.
GitHub
GitHub - jishengpeng/WavChat: A Survey of Spoken Dialogue Models (60 pages)
A Survey of Spoken Dialogue Models (60 pages). Contribute to jishengpeng/WavChat development by creating an account on GitHub.
Small is always nice
https://arxiv.org/abs/2408.13920
Wav2Small: Distilling Wav2Vec2 to 72K parameters for Low-Resource Speech emotion recognition
Dionyssos Kounadis-Bastian, Oliver Schrüfer, Anna Derington, Hagen Wierstorf, Florian Eyben, Felix Burkhardt, Björn Schuller
Speech Emotion Recognition (SER) needs high computational resources to overcome the challenge of substantial annotator disagreement. Today SER is shifting towards dimensional annotations of arousal, dominance, and valence (A/D/V). Universal metrics as the L2 distance prove unsuitable for evaluating A/D/V accuracy due to non converging consensus of annotator opinions. However, Concordance Correlation Coefficient (CCC) arose as an alternative metric for A/D/V where a model's output is evaluated to match a whole dataset's CCC rather than L2 distances of individual audios. Recent studies have shown that wav2vec2 / wavLM architectures outputing a float value for each A/D/V dimension achieve today's State-of-the-art (Sota) CCC on A/D/V. The Wav2Vec2.0 / WavLM family has a high computational footprint, but training small models using human annotations has been unsuccessful. In this paper we use a large Transformer Sota A/D/V model as Teacher/Annotator to train 5 student models: 4 MobileNets and our proposed Wav2Small, using only the Teacher's A/D/V outputs instead of human annotations. The Teacher model we propose also sets a new Sota on the MSP Podcast dataset of valence CCC=0.676. We choose MobileNetV4 / MobileNet-V3 as students, as MobileNet has been designed for fast execution times. We also propose Wav2Small - an architecture designed for minimal parameters and RAM consumption. Wav2Small with an .onnx (quantised) of only 120KB is a potential solution for A/D/V on hardware with low resources, having only 72K parameters vs 3.12M parameters for MobileNet-V4-Small.
https://arxiv.org/abs/2408.13920
Wav2Small: Distilling Wav2Vec2 to 72K parameters for Low-Resource Speech emotion recognition
Dionyssos Kounadis-Bastian, Oliver Schrüfer, Anna Derington, Hagen Wierstorf, Florian Eyben, Felix Burkhardt, Björn Schuller
Speech Emotion Recognition (SER) needs high computational resources to overcome the challenge of substantial annotator disagreement. Today SER is shifting towards dimensional annotations of arousal, dominance, and valence (A/D/V). Universal metrics as the L2 distance prove unsuitable for evaluating A/D/V accuracy due to non converging consensus of annotator opinions. However, Concordance Correlation Coefficient (CCC) arose as an alternative metric for A/D/V where a model's output is evaluated to match a whole dataset's CCC rather than L2 distances of individual audios. Recent studies have shown that wav2vec2 / wavLM architectures outputing a float value for each A/D/V dimension achieve today's State-of-the-art (Sota) CCC on A/D/V. The Wav2Vec2.0 / WavLM family has a high computational footprint, but training small models using human annotations has been unsuccessful. In this paper we use a large Transformer Sota A/D/V model as Teacher/Annotator to train 5 student models: 4 MobileNets and our proposed Wav2Small, using only the Teacher's A/D/V outputs instead of human annotations. The Teacher model we propose also sets a new Sota on the MSP Podcast dataset of valence CCC=0.676. We choose MobileNetV4 / MobileNet-V3 as students, as MobileNet has been designed for fast execution times. We also propose Wav2Small - an architecture designed for minimal parameters and RAM consumption. Wav2Small with an .onnx (quantised) of only 120KB is a potential solution for A/D/V on hardware with low resources, having only 72K parameters vs 3.12M parameters for MobileNet-V4-Small.
arXiv.org
Wav2Small: Distilling Wav2Vec2 to 72K parameters for Low-Resource...
Speech Emotion Recognition (SER) needs high computational resources to overcome the challenge of substantial annotator disagreement. Today SER is shifting towards dimensional annotations of...
Just a reminder that BEST-RQ is a good self-supervised method
https://arxiv.org/abs/2202.01855
Recently added in SpeechBrain too
https://github.com/speechbrain/speechbrain/releases/tag/v1.0.2
Also
https://github.com/HarunoriKawano/BEST-RQ
https://arxiv.org/abs/2202.01855
Recently added in SpeechBrain too
https://github.com/speechbrain/speechbrain/releases/tag/v1.0.2
Also
https://github.com/HarunoriKawano/BEST-RQ
arXiv.org
Self-supervised Learning with Random-projection Quantizer for...
We present a simple and effective self-supervised learning approach for speech recognition. The approach learns a model to predict the masked speech signals, in the form of discrete labels...