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"speech recognition": models, code, and papers

Towards End-to-End Code-Switching Speech Recognition

Nov 01, 2018
Ne Luo, Dongwei Jiang, Shuaijiang Zhao, Caixia Gong, Wei Zou, Xiangang Li

Code-switching speech recognition has attracted an increasing interest recently, but the need for expert linguistic knowledge has always been a big issue. End-to-end automatic speech recognition (ASR) simplifies the building of ASR systems considerably by predicting graphemes or characters directly from acoustic input. In the mean time, the need of expert linguistic knowledge is also eliminated, which makes it an attractive choice for code-switching ASR. This paper presents a hybrid CTC-Attention based end-to-end Mandarin-English code-switching (CS) speech recognition system and studies the effect of hybrid CTC-Attention based models, different modeling units, the inclusion of language identification and different decoding strategies on the task of code-switching ASR. On the SEAME corpus, our system achieves a mixed error rate (MER) of 34.24%.

* 5 pages, submitted to ICASSP 2019 

Learning Discriminative features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition

Jun 19, 2019
Suraj Tripathi, Abhiram Ramesh, Abhay Kumar, Chirag Singh, Promod Yenigalla

This paper proposes a Convolutional Neural Network (CNN) inspired by Multitask Learning (MTL) and based on speech features trained under the joint supervision of softmax loss and center loss, a powerful metric learning strategy, for the recognition of emotion in speech. Speech features such as Spectrograms and Mel-frequency Cepstral Coefficient s (MFCCs) help retain emotion-related low-level characteristics in speech. We experimented with several Deep Neural Network (DNN) architectures that take in speech features as input and trained them under both softmax and center loss, which resulted in highly discriminative features ideal for Speech Emotion Recognition (SER). Our networks also employ a regularizing effect by simultaneously performing the auxiliary task of reconstructing the input speech features. This sharing of representations among related tasks enables our network to better generalize the original task of SER. Some of our proposed networks contain far fewer parameters when compared to state-of-the-art architectures.

* In Progress. arXiv admin note: text overlap with arXiv:1906.05681 

Multi-Channel Transformer Transducer for Speech Recognition

Aug 30, 2021
Feng-Ju Chang, Martin Radfar, Athanasios Mouchtaris, Maurizio Omologo

Multi-channel inputs offer several advantages over single-channel, to improve the robustness of on-device speech recognition systems. Recent work on multi-channel transformer, has proposed a way to incorporate such inputs into end-to-end ASR for improved accuracy. However, this approach is characterized by a high computational complexity, which prevents it from being deployed in on-device systems. In this paper, we present a novel speech recognition model, Multi-Channel Transformer Transducer (MCTT), which features end-to-end multi-channel training, low computation cost, and low latency so that it is suitable for streaming decoding in on-device speech recognition. In a far-field in-house dataset, our MCTT outperforms stagewise multi-channel models with transformer-transducer up to 6.01% relative WER improvement (WERR). In addition, MCTT outperforms the multi-channel transformer up to 11.62% WERR, and is 15.8 times faster in terms of inference speed. We further show that we can improve the computational cost of MCTT by constraining the future and previous context in attention computations.

* Published in INTERSPEECH 2021 

Large vocabulary speech recognition for languages of Africa: multilingual modeling and self-supervised learning

Aug 05, 2022
Sandy Ritchie, You-Chi Cheng, Mingqing Chen, Rajiv Mathews, Daan van Esch, Bo Li, Khe Chai Sim

Almost none of the 2,000+ languages spoken in Africa have widely available automatic speech recognition systems, and the required data is also only available for a few languages. We have experimented with two techniques which may provide pathways to large vocabulary speech recognition for African languages: multilingual modeling and self-supervised learning. We gathered available open source data and collected data for 15 languages, and trained experimental models using these techniques. Our results show that pooling the small amounts of data available in multilingual end-to-end models, and pre-training on unsupervised data can help improve speech recognition quality for many African languages.


An Online Attention-based Model for Speech Recognition

Nov 13, 2018
Ruchao Fan, Pan Zhou, Wei Chen, Jia Jia, Gang Liu

Attention-based end-to-end (E2E) speech recognition models such as Listen, Attend, and Spell (LAS) can achieve better results than traditional automatic speech recognition (ASR) hybrid models on LVCSR tasks. LAS combines acoustic, pronunciation and language model components of a traditional ASR system into a single neural network. However, such architectures are hard to be used for streaming speech recognition for its bidirectional listener architecture and attention mechanism. In this work, we propose to use latency-controlled bidirectional long short-term memory (LC- BLSTM) listener to reduce the delay of forward computing of listener. On the attention side, we propose an adaptive monotonic chunk-wise attention (AMoChA) to make LAS online. We explore how each part performs when it is used alone and obtain comparable or better results than LAS baseline. By combining the above two methods, we successfully stream LAS baseline with only 3.5% relative degradation of character error rate (CER) on our Mandarin corpus. We believe that our methods can also have the same effect on other languages.


Automatic speech recognition for launch control center communication using recurrent neural networks with data augmentation and custom language model

Apr 24, 2018
Kyongsik Yun, Joseph Osborne, Madison Lee, Thomas Lu, Edward Chow

Transcribing voice communications in NASA's launch control center is important for information utilization. However, automatic speech recognition in this environment is particularly challenging due to the lack of training data, unfamiliar words in acronyms, multiple different speakers and accents, and conversational characteristics of speaking. We used bidirectional deep recurrent neural networks to train and test speech recognition performance. We showed that data augmentation and custom language models can improve speech recognition accuracy. Transcribing communications from the launch control center will help the machine analyze information and accelerate knowledge generation.

* SPIE 2018 

Multi-task Recurrent Model for True Multilingual Speech Recognition

Sep 27, 2016
Zhiyuan Tang, Lantian Li, Dong Wang

Research on multilingual speech recognition remains attractive yet challenging. Recent studies focus on learning shared structures under the multi-task paradigm, in particular a feature sharing structure. This approach has been found effective to improve performance on each individual language. However, this approach is only useful when the deployed system supports just one language. In a true multilingual scenario where multiple languages are allowed, performance will be significantly reduced due to the competition among languages in the decoding space. This paper presents a multi-task recurrent model that involves a multilingual speech recognition (ASR) component and a language recognition (LR) component, and the ASR component is informed of the language information by the LR component, leading to a language-aware recognition. We tested the approach on an English-Chinese bilingual recognition task. The results show that the proposed multi-task recurrent model can improve performance of multilingual recognition systems.

* APSIPA 2016. arXiv admin note: text overlap with arXiv:1603.09643 

Is Speech Emotion Recognition Language-Independent? Analysis of English and Bangla Languages using Language-Independent Vocal Features

Nov 21, 2021
Fardin Saad, Hasan Mahmud, Md. Alamin Shaheen, Md. Kamrul Hasan, Paresha Farastu

A language agnostic approach to recognizing emotions from speech remains an incomplete and challenging task. In this paper, we used Bangla and English languages to assess whether distinguishing emotions from speech is independent of language. The following emotions were categorized for this study: happiness, anger, neutral, sadness, disgust, and fear. We employed three Emotional Speech Sets, of which the first two were developed by native Bengali speakers in Bangla and English languages separately. The third was the Toronto Emotional Speech Set (TESS), which was developed by native English speakers from Canada. We carefully selected language-independent prosodic features, adopted a Support Vector Machine (SVM) model, and conducted three experiments to carry out our proposition. In the first experiment, we measured the performance of the three speech sets individually. This was followed by the second experiment, where we recorded the classification rate by combining the speech sets. Finally, in the third experiment we measured the recognition rate by training and testing the model with different speech sets. Although this study reveals that Speech Emotion Recognition (SER) is mostly language-independent, there is some disparity while recognizing emotional states like disgust and fear in these two languages. Moreover, our investigations inferred that non-native speakers convey emotions through speech, much like expressing themselves in their native tongue.

* 9 pages, 7 figures, currently under review in International Journal of Advanced Computer Science and Applications (IJACSA) 

Extending RNN-T-based speech recognition systems with emotion and language classification

Jul 28, 2022
Zvi Kons, Hagai Aronowitz, Edmilson Morais, Matheus Damasceno, Hong-Kwang Kuo, Samuel Thomas, George Saon

Speech transcription, emotion recognition, and language identification are usually considered to be three different tasks. Each one requires a different model with a different architecture and training process. We propose using a recurrent neural network transducer (RNN-T)-based speech-to-text (STT) system as a common component that can be used for emotion recognition and language identification as well as for speech recognition. Our work extends the STT system for emotion classification through minimal changes, and shows successful results on the IEMOCAP and MELD datasets. In addition, we demonstrate that by adding a lightweight component to the RNN-T module, it can also be used for language identification. In our evaluations, this new classifier demonstrates state-of-the-art accuracy for the NIST-LRE-07 dataset.

* Accepted for publication in Interspeech 2022 

Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition

May 17, 2022
Zengrui Jin, Mengzhe Geng, Jiajun Deng, Tianzi Wang, Shujie Hu, Guinan Li, Xunying Liu

Despite the rapid progress of automatic speech recognition (ASR) technologies targeting normal speech, accurate recognition of dysarthric and elderly speech remains highly challenging tasks to date. It is difficult to collect large quantities of such data for ASR system development due to the mobility issues often found among these users. To this end, data augmentation techniques play a vital role. In contrast to existing data augmentation techniques only modifying the speaking rate or overall shape of spectral contour, fine-grained spectro-temporal differences between dysarthric, elderly and normal speech are modelled using a novel set of speaker dependent (SD) generative adversarial networks (GAN) based data augmentation approaches in this paper. These flexibly allow both: a) temporal or speed perturbed normal speech spectra to be modified and closer to those of an impaired speaker when parallel speech data is available; and b) for non-parallel data, the SVD decomposed normal speech spectral basis features to be transformed into those of a target elderly speaker before being re-composed with the temporal bases to produce the augmented data for state-of-the-art TDNN and Conformer ASR system training. Experiments are conducted on four tasks: the English UASpeech and TORGO dysarthric speech corpora; the English DementiaBank Pitt and Cantonese JCCOCC MoCA elderly speech datasets. The proposed GAN based data augmentation approaches consistently outperform the baseline speed perturbation method by up to 0.91% and 3.0% absolute (9.61% and 6.4% relative) WER reduction on the TORGO and DementiaBank data respectively. Consistent performance improvements are retained after applying LHUC based speaker adaptation.

* arXiv admin note: text overlap with arXiv:2202.10290