The PASCAL Visual Object Classes Challenge

We present SpecAugment, a simple data augmentation method for speech recognition. SpecAugment is applied directly to the feature inputs of a neural network (i.e., filter bank coefficients). The augmentation policy consists of warping the features, masking blocks of frequency channels, and masking blocks of time steps. We apply SpecAugment on Listen, Attend and Spell networks for end-to-end speech recognition tasks. We achieve state-of-the-art performance on the LibriSpeech 960h and Swichboard 300h tasks, outperforming all prior work. On LibriSpeech, we achieve 6.8% WER on test-other without the use of a language model, and 5.8% WER with shallow fusion with a language model. This compares to the previous state-of-the-art hybrid system of 7.5% WER. For Switchboard, we achieve 7.2%/14.6% on the Switchboard/CallHome portion of the Hub5'00 test set without the use of a language model, and 6.8%/14.1% with shallow fusion, which compares to the previous state-of-the-art hybrid system at 8.3%/17.3% WER.

SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

National Institute of Standards and Technology における超伝導研究及び生活

In this paper, we use data augmentation to improve performance of deep neural network (DNN) embeddings for speaker recognition. The DNN, which is trained to discriminate between speakers, maps variable-length utterances to fixed-dimensional embeddings that we call x-vectors. Prior studies have found that embeddings leverage large-scale training datasets better than i-vectors. However, it can be challenging to collect substantial quantities of labeled data for training. We use data augmentation, consisting of added noise and reverberation, as an inexpensive method to multiply the amount of training data and improve robustness. The x-vectors are compared with i-vector baselines on Speakers in the Wild and NIST SRE 2016 Cantonese. We find that while augmentation is beneficial in the PLDA classifier, it is not helpful in the i-vector extractor. However, the x-vector DNN effectively exploits data augmentation, due to its supervised training. As a result, the x-vectors achieve superior performance on the evaluation datasets.

X-Vectors: Robust DNN Embeddings for Speaker Recognition

Digital processing of speech signals

The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough and breathing difficulties. We foresee that respiratory sounds, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection, and in the near future this can help to diagnose COVID-19. Â© 2020 ISCA

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Coswara - A database of breathing, cough, and voice sounds for COVID-19 diagnosis

This paper introduces the second DIHARD challenge, the second in a series of speaker diarization challenges intended to improve the robustness of diarization systems to variation in recording equipment, noise conditions, and conversational domain. The challenge comprises four tracks evaluating diarization performance under two input conditions (single channel vs. multi-channel) and two segmentation conditions (diarization from a reference speech segmentation vs. diarization from scratch). In order to prevent participants from overtuning to a particular combination of recording conditions and conversational domain, recordings are drawn from a variety of sources ranging from read audiobooks to meeting speech, to child language acquisition recordings, to dinner parties, to web video. We describe the task and metrics, challenge design, datasets, and baseline systems for speech enhancement, speech activity detection, and diarization.

The Second DIHARD Diarization Challenge: Dataset, Task, and Baselines.

We introduce a new approach to training multilayer perceptrons (MLPs) for large vocabulary continuous speech recognition (LVCSR) in new languages which have only few hours of annotated in-domain training data (for example, 1 hour of data). In our approach, large amounts of annotated out-of-domain data from multiple languages are used to train multilingual MLP systems without dealing with the different phoneme sets for these languages. Features extracted from these MLP systems are used to train LVCSR systems in the low-resource language similar to the Tandem approach. In our experiments, the proposed features provide a relative improvement of about 30% in an low-resource LVCSR setting with only one hour of training data.

Multilingual MLP features for low-resource LVCSR systems

Convolutional neural networks (CNN) are extensions to deep neural networks (DNN) which are used as alternate acoustic models with state-of-the-art performances for speech recognition. In this paper, CNNs are used as acoustic models for speech activity detection (SAD) on data collected over noisy radio communication channels. When these SAD models are tested on audio recorded from radio channels not seen during training, there is severe performance degradation. We attribute this degradation to mismatches between the two dimensional filters learnt in the initial CNN layers and the novel channel data. Using a small amount of supervised data from the novel channels, the filters can be adapted to provide significant improvements in SAD performance. In mismatched acoustic conditions, the adapted models provide significant improvements (about 10-25%) relative to conventional DNN-based SAD systems. These results illustrate that CNNs have a considerable advantage in fast adaptation for acoustic modeling in these settings.

Analyzing convolutional neural networks for speech activity detection in mismatched acoustic conditions

Performance of a typical automatic speech recognition (ASR) system severely degrades when it encounters speech from reverberant environments. Part of the reason for this degradation is the feature extraction techniques that use analysis windows which are much shorter than typical room impulse responses. We present a feature extraction technique based on modeling temporal envelopes of the speech signal in narrow subbands using frequency domain linear prediction (FDLP). FDLP provides an all-pole approximation of the Hilbert envelope of the signal obtained by linear prediction on cosine transform of the signal. ASR experiments on speech data degraded with a number of room impulse responses (with varying degrees of distortion) show significant performance improvements for the proposed FDLP features when compared to other robust feature extraction techniques (average relative reduction of 24% in word error rate). Similar improvements are also obtained for far-field data which contain natural reverberation in background noise. These results are achieved without any noticeable degradation in performance for clean speech.

/pdf/recognition-of-reverberant-speech-using-frequency-domain-1358d85mx9.pdf

Sriram Ganapathy

Papers

Coswara - A database of breathing, cough, and voice sounds for COVID-19 diagnosis

The Second DIHARD Diarization Challenge: Dataset, Task, and Baselines.

Multilingual MLP features for low-resource LVCSR systems

Analyzing convolutional neural networks for speech activity detection in mismatched acoustic conditions

Recognition of Reverberant Speech Using Frequency Domain Linear Prediction