In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. In order to reduce the mismatch between labeled source domain data ("standard" accent) and unlabeled target domain data (with heavy accents), we augment the learning objective for a Kaldi TDNN network with a domain adversarial training (DAT) objective to encourage the model to learn accent-invariant features. In experiments with three Mandarin accents, we show that DAT yields up to 7.45% relative character error rate reduction when we do not have transcriptions of the accented speech, compared with the baseline trained on standard accent data only. We also find a benefit from DAT when used in combination with training from automatic transcriptions on the accented data. Furthermore, we find that DAT is superior to multi-task learning for accented speech recognition.
Automatic Speech Recognition (ASR) systems generalize poorly on accented speech. The phonetic and linguistic variability of accents present hard challenges for ASR systems today in both data collection and modeling strategies. The resulting bias in ASR performance across accents comes at a cost to both users and providers of ASR. We present a survey of current promising approaches to accented speech recognition and highlight the key challenges in the space. Approaches mostly focus on single model generalization and accent feature engineering. Among the challenges, lack of a standard benchmark …
Automatic Speech Recognition (ASR) systems are often optimized to work best for speakers with canonical speech patterns. Unfortunately, these systems perform poorly when tested on atypical speech and heavily accented speech. It has previously been shown that personalization through model fine-tuning substantially improves performance. However, maintaining such large models per speaker is costly and difficult to scale. We show that by adding a relatively small number of extra parameters to the encoder layers via so-called residual adapter, we can achieve similar adaptation gains compared to mod…
While improvements have been made in automatic speech recognition performance over the last several years, machines continue to have significantly lower performance on accented speech than humans. In addition, the most significant improvements on accented speech primarily arise by overwhelming the problem with hundreds or even thousands of hours of data. Humans typically require much less data to adapt to a new accent. This paper explores methods that are inspired by human perception to evaluate possible performance improvements for recognition of accented speech, with a specific focus on reco…
AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR
Mila Quebec AI Institute（米拉魁北克人工智能研究所）；Lanfrica；Lelapa AI；Instituto Politécnico Nacional（国立理工学院）；Masakhane NLP（Masakhane自然语言处理组织）；Intron Health（Intron健康公司）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；AI Saturdays Lagos（拉各斯AI周六组织）；Karya Inc（Karya公司）；Ford Motor Company（福特汽车公司）；McGill University（麦吉尔大学）；University of Deutso（德乌措大学）；University of Colorado Colorado Springs（科罗拉多大学科罗拉多斯普林斯分校）；University of Minnesota（明尼苏达大学）
针对非洲口音临床与通用 ASR 数据及基准缺失问题，本文发布涵盖 13 国 120 种口音的 200 小时 AfriSpeech 数据集、测试基准及 SOTA 预训练模型。
Conventional conversation assistants extract text transcripts from the speech signal using automatic speech recognition (ASR) and then predict intent from the transcriptions. Using end-to-end spoken language understanding (SLU), the intents of the speaker are predicted directly from the speech signal without requiring intermediate text transcripts. As a result, the model can optimize directly for intent classification and avoid cascading errors from ASR. The end-to-end SLU system also helps in reducing the latency of the intent prediction model. Although many datasets are available publicly fo…
Google（谷歌）；ALS Therapy Development Institute（肌萎缩侧索硬化症治疗发展研究所）
Automatic speech recognition (ASR) systems have dramatically improved over the last few years. ASR systems are most often trained from 'typical' speech, which means that underrepresented groups don't experience the same level of improvement. In this paper, we present and evaluate finetuning techniques to improve ASR for users with non-standard speech. We focus on two types of non-standard speech: speech from people with amyotrophic lateral sclerosis (ALS) and accented speech. We train personalized models that achieve 62% and 35% relative WER improvement on these two groups, bringing the absolu…
Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near-human performance on a wide variety of datasets. However, they still lack generalization capability and are not robust to domain shifts like accent variations. In this work, we use speech audio representing four different French accents to create fine-tuning datasets that improve the robustness of pre-trained ASR models. By incorporating various accents in the training set, we obtain both in-domain and out-of-domain improvements. Our numerical experiments show that we can reduce error ra…
Performant ASR Models for Medical Entities in Accented Speech
CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；Intron Health；Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学与应用实验室）；University of Minnesota - Twin Cities（明尼苏达大学双城分校）；African Masters of Machine Intelligence（非洲机器智能硕士项目）；AIMS/AMMI（非洲数学科学研究所/非洲机器智能硕士项目）；AI Saturdays Lagos（拉各斯AI周六）；Masakhane NLP（Masakhane自然语言处理项目）
Carnegie Mellon University（卡内基梅隆大学）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Indian Institute of Science（印度科学学院）
The problem of automatic accent identification is important for several applications like speaker profiling and recognition as well as for improving speech recognition systems. The accented nature of speech can be primarily attributed to the influence of the speaker's native language on the given speech recording. In this paper, we propose a novel accent identification system whose training exploits speech in native languages along with the accented speech. Specifically, we develop a deep Siamese network-based model which learns the association between accented speech recordings and the native…
We introduce the problem of adapting a black-box, cloud-based ASR system to speech from a target accent. While leading online ASR services obtain impressive performance on main-stream accents, they perform poorly on sub-populations - we observed that the word error rate (WER) achieved by Google's ASR API on Indian accents is almost twice the WER on US accents. Existing adaptation methods either require access to model parameters or overlay an error-correcting module on output transcripts. We highlight the need for correlating outputs with the original speech to fix accent errors. Accordingly, …
In this paper, we present several adaptation methods for non-native speech recognition. We have tested pronunciation modelling, MLLR and MAP non-native pronunciation adaptation and HMM models retraining on the HIWIRE foreign accented English speech database. The ``phonetic confusion'' scheme we have developed consists in associating to each spoken phone several sequences of confused phones. In our experiments, we have used different combinations of acoustic models representing the canonical and the foreign pronunciations: spoken and native models, models adapted to the non-native accent with M…
The awareness for biased ASR datasets or models has increased notably in recent years. Even for English, despite a vast amount of available training data, systems perform worse for non-native speakers. In this work, we improve an accent-conversion model (ACM) which transforms native US-English speech into accented pronunciation. We include phonetic knowledge in the ACM training to provide accurate feedback about how well certain pronunciation patterns were recovered in the synthesized waveform. Furthermore, we investigate the feasibility of learned accent representations instead of static embe…
In this paper, we present our overall efforts to improve the performance of a code-switching speech recognition system using semi-supervised training methods from lexicon learning to acoustic modeling, on the South East Asian Mandarin-English (SEAME) data. We first investigate semi-supervised lexicon learning approach to adapt the canonical lexicon, which is meant to alleviate the heavily accented pronunciation issue within the code-switching conversation of the local area. As a result, the learned lexicon yields improved performance. Furthermore, we attempt to use semi-supervised training to …
Quantifying Speaker Embedding Phonological Rule Interactions in Accented Speech Synthesis
Signal Analysis and Interpretation Lab, University of Southern California（南加州大学信号分析与解释实验室）；Thomas Lord Department of Computer Science, University of Southern California（南加州大学托马斯·劳德计算机科学系）；Department of Linguistics, University of Southern California（南加州大学语言学系）
本研究通过量化说话者嵌入与语音规则的相互作用，提出 PSR 度量标准，揭示口音生成中规则与身份的纠缠关系。
Learning-free L2-Accented Speech Generation using Phonological Rules
Signal Analysis and Interpretation Lab, University of Southern California, USA（信号分析与解读实验室，美国南加州大学）；Department of Linguistics, University of Southern California（语言学系，美国南加州大学）
Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data
Signal Analysis and Interpretation Lab, University of Southern California, USA（信号分析与解释实验室，南加州大学）；Thomas Lord Department of Computer Science, University of Southern California, USA（托马斯·劳德计算机科学系，南加州大学）；Department of Linguistics, University of Southern California（语言学系，南加州大学）
CommonAccent: Exploring Large Acoustic Pretrained Models for Accent Classification Based on Common Voice
Idiap Research Institute（Idiap研究所）；Ecole Polytechnique Federale de Lausanne（洛桑联邦理工学院）；Texas A&M University（得克萨斯农工大学）；Vilnius Gediminas Technical University（维尔纽斯盖迪米纳斯技术大学）；Université Laval（拉瓦尔大学）；Concordia University（康考迪亚大学）；Mila-Québec AI Institute（米拉-魁北克人工智能研究所）
The University of Edinburgh（爱丁堡大学）；Google Research（谷歌研究院）
Speech-based image retrieval has been studied as a proxy for joint representation learning, usually without emphasis on retrieval itself. As such, it is unclear how well speech-based retrieval can work in practice -- both in an absolute sense and versus alternative strategies that combine automatic speech recognition (ASR) with strong text encoders. In this work, we extensively study and expand choices of encoder architectures, training methodology (including unimodal and multimodal pretraining), and other factors. Our experiments cover different types of speech in three datasets: Flickr Audio…
Imperial College London（帝国理工学院）；University of Southampton（南安普顿大学）
Speech recognition systems have improved dramatically over the last few years, however, their performance is significantly degraded for the cases of accented or impaired speech. This work explores domain adversarial neural networks (DANN) for speaker-independent speech recognition on the UAS dataset of dysarthric speech. The classification task on 10 spoken digits is performed using an end-to-end CNN taking raw audio as input. The results are compared to a speaker-adaptive (SA) model as well as speaker-dependent (SD) and multi-task learning models (MTL). The experiments conducted in this paper…
University of Maryland, College Park（马里兰大学帕克分校）；SRI International（SRI国际）；Apple Inc.（苹果公司）
This paper explores the use of multi-view features and their discriminative transforms in a convolutional deep neural network (CNN) architecture for a continuous large vocabulary speech recognition task. Mel-filterbank energies and perceptually motivated forced damped oscillator coefficient (DOC) features are used after feature-space maximum-likelihood linear regression (fMLLR) transforms, which are combined and fed as a multi-view feature to a single CNN acoustic model. Use of multi-view feature representation demonstrated significant reduction in word error rates (WERs) compared to the use o…
Inner Mongolia University（内蒙古大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Bremen（不来梅大学）
Accented text-to-speech (TTS) synthesis seeks to generate speech with an accent (L2) as a variant of the standard version (L1). Accented TTS synthesis is challenging as L2 is different from L1 in both in terms of phonetic rendering and prosody pattern. Furthermore, there is no easy solution to the control of the accent intensity in an utterance. In this work, we propose a neural TTS architecture, that allows us to control the accent and its intensity during inference. This is achieved through three novel mechanisms, 1) an accent variance adaptor to model the complex accent variance with three …
We report on adaptation of multilingual end-to-end speech recognition models trained on as many as 100 languages. Our findings shed light on the relative importance of similarity between the target and pretraining languages along the dimensions of phonetics, phonology, language family, geographical location, and orthography. In this context, experiments demonstrate the effectiveness of two additional pretraining objectives in encouraging language-independent encoder representations: a context-independent phoneme objective paired with a language-adversarial classification objective.
As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems. In this paper, our goal is to build a unified end-to-end speech recognition system that generalizes well across accents. For this purpose, we propose a novel pre-training framework AIPNet based on generative adversarial nets (GAN) for accent-invariant representation learning: Accent Invariant Pre-training Networks. We pre-train AIPNet to disentangle accent-invariant and accent-specific characteristics from acoustic features through adversarial training on acc…
This paper describes a general, scalable, end-to-end framework that uses the generative adversarial network (GAN) objective to enable robust speech recognition. Encoders trained with the proposed approach enjoy improved invariance by learning to map noisy audio to the same embedding space as that of clean audio. Unlike previous methods, the new framework does not rely on domain expertise or simplifying assumptions as are often needed in signal processing, and directly encourages robustness in a data-driven way. We show the new approach improves simulated far-field speech recognition of vanilla…
National Key Laboratory of Air Traffic Control Automation System Technology, Sichuan University（四川大学）；Southwest Air Traffic Management Bureau, Civil Aviation Administration of China (CAAC)（中国民用航空局西南空中交通管理局）；Wisesoft Co., Ltd.（四川川大智胜软件股份有限公司）
Automatic Speech Recognition (ASR) is greatly developed in recent years, which expedites many applications on other fields. For the ASR research, speech corpus is always an essential foundation, especially for the vertical industry, such as Air Traffic Control (ATC). There are some speech corpora for common applications, public or paid. However, for the ATC, it is difficult to collect raw speeches from real systems due to safety issues. More importantly, for a supervised learning task like ASR, annotating the transcription is a more laborious work, which hugely restricts the prospect of ASR ap…
The usage of automatic speech recognition (ASR) systems are becoming omnipresent ranging from personal assistant to chatbots, home, and industrial automation systems, etc. Modern robots are also equipped with ASR capabilities for interacting with humans as speech is the most natural interaction modality. However, ASR in robots faces additional challenges as compared to a personal assistant. Being an embodied agent, a robot must recognize the physical entities around it and therefore reliably recognize the speech containing the description of such entities. However, current ASR systems are ofte…
Accented speech recognition and accent classification are relatively under-explored research areas in speech technology. Recently, deep learning-based methods and Transformer-based pretrained models have achieved superb performances in both areas. However, most accent classification tasks focused on classifying different kinds of English accents and little attention was paid to geographically-proximate accent classification, especially under a low-resource setting where forensic speech science tasks usually encounter. In this paper, we explored three main accent modelling methods combined with…
Acoustic and perceptual differences between standard and accented speech and their voice clones
Department of Linguistics, University at Buffalo, United States（语言学系，布法罗大学，美国）；Department of Computer Science and Engineering, University at Buffalo, United States（计算机科学与工程系，布法罗大学，美国）；Emeritus Faculty, Australian National University, Australia（澳大利亚国立大学荣誉教职）
Tsinghua University（清华大学）；IDG/McGovern Institute of Brain Research（IDG/麦戈文脑科学研究院）
Automatic speech recognition (ASR) systems based on deep neural networks are weak against adversarial perturbations. We propose mixPGD adversarial training method to improve the robustness of the model for ASR systems. In standard adversarial training, adversarial samples are generated by leveraging supervised or unsupervised methods. We merge the capabilities of both supervised and unsupervised approaches in our method to generate new adversarial samples which aid in improving model robustness. Extensive experiments and comparison across various state-of-the-art defense methods and adversaria…
KAIST（韩国科学技术院）；Mokwon University（木原大学）；KAIST Institute for Artificial Intelligence（韩国科学技术院人工智能研究所）
Many speech enhancement methods try to learn the relationship between noisy and clean speech, obtained using an acoustic room simulator. We point out several limitations of enhancement methods relying on clean speech targets; the goal of this work is proposing an alternative learning algorithm, called acoustic and adversarial supervision (AAS). AAS makes the enhanced output both maximizing the likelihood of transcription on the pre-trained acoustic model and having general characteristics of clean speech, which improve generalization on unseen noisy speeches. We employ the connectionist tempor…
Dysarthria is a motor speech impairment affecting millions of people. Dysarthric speech can be far less intelligible than those of non-dysarthric speakers, causing significant communication difficulties. The goal of our work is to develop a model for dysarthric to healthy speech conversion using Cycle-consistent GAN. Using 18,700 dysarthric and 8,610 healthy control Korean utterances that were recorded for the purpose of automatic recognition of voice keyboard in a previous study, the generator is trained to transform dysarthric to healthy speech in the spectral domain, which is then converted…
Institute for Natural Language Processing, University of Stuttgart（斯图加特大学自然语言处理研究所）
We investigate densely connected convolutional networks (DenseNets) and their extension with domain adversarial training for noise robust speech recognition. DenseNets are very deep, compact convolutional neural networks which have demonstrated incredible improvements over the state-of-the-art results in computer vision. Our experimental results reveal that DenseNets are more robust against noise than other neural network based models such as deep feed forward neural networks and convolutional neural networks. Moreover, domain adversarial learning can further improve the robustness of DenseNet…
University of Chicago（芝加哥大学）；University of Florida（佛罗里达大学）
An adversarial attack is an exploitative process in which minute alterations are made to natural inputs, causing the inputs to be misclassified by neural models. In the field of speech recognition, this has become an issue of increasing significance. Although adversarial attacks were originally introduced in computer vision, they have since infiltrated the realm of speech recognition. In 2017, a genetic attack was shown to be quite potent against the Speech Commands Model. Limited-vocabulary speech classifiers, such as the Speech Commands Model, are used in a variety of applications, particula…
Georgia Institute of Technology（佐治亚理工学院）；IBM Research（IBM研究院）
Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems. In this work, we present a U-Net based attention model, U-Net$_{At}$, to enhance adversarial speech signals. Specifically, we evaluate the model performance by interpretable speech recognition metrics and discuss the model performance by the augmented adversarial training. Our experiments show that our proposed U-Net$_{At}$ improves the perceptual evaluation of speech quality (PESQ) from 1.13 to 2.78, speech transmission index (STI) from 0.65 to 0.75, sh…
Northwestern Polytechnical University（西北工业大学）；Mobvoi AI Lab（出门问问人工智能实验室）；University of Washington（华盛顿大学）
This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models. During training, the fast gradient sign method is used to generate adversarial examples augmenting the original training data. Different from conventional data augmentation based on data transformations, the examples are dynamically generated based on current acoustic model parameters. We assess the impact of adversarial data augmentation in experiments on the Aurora-4 and CHiME-4 single-channel tasks, showing improved robustness against nois…
Aalto University（阿尔托大学）；National Institute of Informatics（国立信息学研究所）；NTT Communication Science Laboratories, NTT Corporation（NTT公司NTT通信科学实验室）
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech synthesis. First, we predict fundamental frequency and voicing information from MFCCs with an autoregressive recurrent neural net. Second, the spectral envelope information contained in MFCCs is converted to all-pole filters, and a pitch-synchronous excitation model matched to these filters is trained. Finally, we introduce a generative adversarial network -based noise model to …
Institute for Natural Language Processing, University of Stuttgart（斯图加特大学自然语言处理研究所）
In this paper, we investigate the use of adversarial learning for unsupervised adaptation to unseen recording conditions, more specifically, single microphone far-field speech. We adapt neural networks based acoustic models trained with close-talk clean speech to the new recording conditions using untranscribed adaptation data. Our experimental results on Italian SPEECON data set show that our proposed method achieves 19.8% relative word error rate (WER) reduction compared to the unadapted models. Furthermore, this adaptation method is beneficial even when performed on data from another langua…
Modern automatic speech recognition (ASR) systems have achieved superhuman Word Error Rate (WER) on many common corpora despite lacking adequate performance on speech in the wild. Beyond that, there is a lack of real-world, accented corpora to properly benchmark academic and commercial models. To ensure this type of speech is represented in ASR benchmarking, we present Earnings-22, a 125 file, 119 hour corpus of English-language earnings calls gathered from global companies. We run a comparison across 4 commercial models showing the variation in performance when taking country of origin into c…
Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond
Intron（英创公司）；BioRAMP（BioRAMP机构）；Brown University（布朗大学）；Indian Institute of Information Technology Allahabad（阿拉哈巴德印度信息技术学院）；University of Minnesota-Twin Cities（明尼苏达大学双城分校）；University of Glasgow（格拉斯哥大学）；University of Florida（佛罗里达大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Georgia Institute of Technology（佐治亚理工学院）
该工作提出 Afrispeech-Dialog 基准数据集，通过 50 段非洲口音英语对话评估 ASR 和说话人分离系统，发现口音导致性能下降超 10%，并揭示 ASR 错误对医疗摘要的影响，呼吁构建更具包容性的低资源语音数据集。
Previous accent classification research focused mainly on detecting accents with pure acoustic information without recognizing accented speech. This work combines phonetic knowledge such as vowels with acoustic information to build Guassian Mixture Model (GMM) classifier with Perceptual Linear Predictive (PLP) features, optimized by Hetroscedastic Linear Discriminant Analysis (HLDA). With input about 20-second accented speech, this system achieves classification rate of 51% on a 7-way classification system focusing on the major types of accents in English, which is competitive to the state-of-…
Domain-Aware Speaker Diarization On African-Accented English
Department of Computer Science, University of Florida, Gainesville（佛罗里达大学计算机科学系）；Department of Computer Science, University of Colorado Boulder, Boulder（科罗拉多州立大学博尔德分校计算机科学系）
Université de Lorraine（洛林大学）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；LORIA（洛林计算机科学与应用实验室）；Intron Health（Intron健康公司）；Amazethu Research（Amazethu研究所）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；AI Saturdays Lagos（拉各斯AI周六组织）；University of Pretoria（比勒陀利亚大学）；University of Minnesota - Twin Cities（明尼苏达大学双城分校）；Luleå University of Technology（吕勒奥理工大学）；Masakhane NLP（马萨卡内自然语言处理组织）
