Twin Regularization for Online Speech Recognition

Ravanelli, Mirco; Serdyuk, Dmitriy; Bengio, Yoshua

doi:10.21437/interspeech.2018-1407

Cited by 12 publications

(8 citation statements)

References 34 publications

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“…1, a standard CNN pipeline (pooling, normalization, activations, dropout) can be employed after the first sinc-based convolution. Multiple standard convolutional, fully-connected or recurrent layers [37][38][39][40] can then be stacked together to finally perform a speaker classification with a softmax classifier.…”

Section: The Sincnet Architecturementioning

confidence: 99%

Speaker Recognition from Raw Waveform with SincNet

Ravanelli

Bengio

2018

2018 IEEE Spoken Language Technology Workshop (SLT)

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Deep neural networks can learn complex and abstract representations, that are progressively obtained by combining simpler ones. A recent trend in speech and speaker recognition consists in discovering these representations starting from raw audio samples directly. Differently from standard hand-crafted features such as MFCCs or FBANK, the raw waveform can potentially help neural networks discover better and more customized representations. The highdimensional raw inputs, however, can make training significantly more challenging.This paper summarizes our recent efforts to develop a neural architecture that efficiently processes speech from audio waveforms. In particular, we propose SincNet, a novel Convolutional Neural Network (CNN) that encourages the first layer to discover meaningful filters by exploiting parametrized sinc functions. In contrast to standard CNNs, which learn all the elements of each filter, only low and high cutoff frequencies of band-pass filters are directly learned from data. This inductive bias offers a very compact way to derive a customized front-end, that only depends on some parameters with a clear physical meaning.Our experiments, conducted on both speaker and speech recognition, show that the proposed architecture converges faster, performs better, and is more computationally efficient than standard CNNs.

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Section: The Sincnet Architecturementioning

confidence: 99%

Speaker Recognition from Raw Waveform with SincNet

Ravanelli

Bengio

2018

2018 IEEE Spoken Language Technology Workshop (SLT)

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625

500

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“…The current version supports standard MLPs, CNNs, RNNs, LSTM, and GRU models. Moreover, it supports some advanced recurrent architectures, such as the recently-proposed Light GRU [31] and twin-regularized RNNs [32]. The SincNet model [33,34] is also implemented to perform speech recognition from raw waveform directly.…”

Section: Dnn Acoustic Modelingmentioning

confidence: 99%

The Pytorch-kaldi Speech Recognition Toolkit

Ravanelli¹,

Parcollet²,

Bengio³

2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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222

135

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The availability of open-source software is playing a remarkable role in the popularization of speech recognition and deep learning. Kaldi, for instance, is nowadays an established framework used to develop state-of-the-art speech recognizers. PyTorch is used to build neural networks with the Python language and has recently spawn tremendous interest within the machine learning community thanks to its simplicity and flexibility.The PyTorch-Kaldi project aims to bridge the gap between these popular toolkits, trying to inherit the efficiency of Kaldi and the flexibility of PyTorch. PyTorch-Kaldi is not only a simple interface between these software, but it embeds several useful features for developing modern speech recognizers. For instance, the code is specifically designed to naturally plug-in user-defined acoustic models. As an alternative, users can exploit several pre-implemented neural networks that can be customized using intuitive configuration files. PyTorch-Kaldi supports multiple feature and label streams as well as combinations of neural networks, enabling the use of complex neural architectures. The toolkit is publicly-released along with a rich documentation and is designed to properly work locally or on HPC clusters.Experiments, that are conducted on several datasets and tasks, show that PyTorch-Kaldi can effectively be used to develop modern state-of-the-art speech recognizers.

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“…The results show that the proposed system performance gap between CIphone CTC and the A2W model is reduced. Ravanelli et al [266] performed experiments on TIMIT, DIRHA, CHiME, and LibriSpeech databases. Hybrid feature extraction techniques were employed using MFCC, fBANKS, and FMLLR to train the RNN-HMM system, and a significant improvement over standard RNN systems has been reported.…”

Section: Englishmentioning

confidence: 99%

Computational intelligence in processing of speech acoustics: a survey

Singh

Kaur

Kukreja

et al. 2022

Complex Intell. Syst.

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Speech recognition of a language is a key area in the field of pattern recognition. This paper presents a comprehensive survey on the speech recognition techniques for non-Indian and Indian languages, and compiled some of the computational models used for processing speech acoustics. An immense number of frameworks are available for speech processing and recognition for languages persisting around the globe. However, a limited number of automatic speech recognition systems are available for commercial use. The gap between the languages being spoken around the globe and the technical support available to these languages are very few. This paper examined major challenges for speech recognition for different languages. Analysis of the literature shows that lack of standard databases availability of minority languages hinder the research recognition research across the globe. When compared with non-Indian languages, the research on speech recognition of Indian languages (except Hindi) has not achieved the expected milestone yet. Combination of MFCC and DNN–HMM classifier is most commonly used system for developing ASR minority languages, whereas in some of the majority languages, researchers are using much advance algorithms of DNN. It has also been observed that the research in this field is quite thin and still more research needs to be carried out, particularly in the case of minority languages.

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Twin Regularization for Online Speech Recognition

Cited by 12 publications

References 34 publications

Speaker Recognition from Raw Waveform with SincNet

Speaker Recognition from Raw Waveform with SincNet

The Pytorch-kaldi Speech Recognition Toolkit

Computational intelligence in processing of speech acoustics: a survey

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