pwc-archive/papers-with-abstracts huggingface dataset:
The SAFE-T Corpus: A New Resource for Simulated Public Safety Communications
Authors: Dana Delgado, Kevin Walker, Stephanie Strassel, Karen Jones, Christopher Caruso, David Graff
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Action Detection, Activity Detection, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We introduce a new resource, the SAFE-T (Speech Analysis for Emergency Response Technology) Corpus, designed to simulate first-responder communications by inducing high vocal effort and urgent speech with situational background noise in a game-based collection protocol. Linguistic Data Consortium developed the SAFE-T Corpus to support the NIST (National Institute of Standards and Technology) OpenSAT (Speech Analytic Technologies) evaluation series, whose goal is to advance speech analytic technologies including automatic speech recognition, speech activity detection and keyword search in multiple domains including simulated public safety communications data. The corpus comprises over 300 hours of audio from 115 unique speakers engaged in a collaborative problem-solving activity representative of public safety communications in terms of speech content, noise types and noise levels. Portions of the corpus have been used in the OpenSAT 2019 evaluation and the full corpus will be published in the LDC catalog. We describe the design and implementation of the SAFE-T Corpus collection, discuss the approach of capturing spontaneous speech from study participants through game-based speech collection, and report on the collection results including several challenges associated with the collection.
Lexical Tone Recognition in Mizo using Acoustic-Prosodic Features
Authors: Parismita Gogoi, Abhishek Dey, Wendy Lalhminghlui, Priyankoo Sarmah, S R Mahadeva Prasanna
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Mizo is an under-studied Tibeto-Burman tonal language of the North-East India. Preliminary research findings have confirmed that four distinct tones of Mizo (High, Low, Rising and Falling) appear in the language. In this work, an attempt is made to automatically recognize four phonological tones in Mizo distinctively using acoustic-prosodic parameters as features. Six features computed from Fundamental Frequency (F0) contours are considered and two classifier models based on Support Vector Machine (SVM) {\&} Deep Neural Network (DNN) are implemented for automatic tonerecognition task respectively. The Mizo database consists of 31950 iterations of the four Mizo tones, collected from 19 speakers using trisyllabic phrases. A four-way classification of tones is attempted with a balanced (equal number of iterations per tone category) dataset for each tone of Mizo. it is observed that the DNN based classifier shows comparable performance in correctly recognizing four phonological Mizo tones as of the SVM based classifier.
Artie Bias Corpus: An Open Dataset for Detecting Demographic Bias in Speech Applications
Authors: Josh Meyer, Lindy Rauchenstein, Joshua D. Eisenberg, Nicholas Howell
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We describe the creation of the Artie Bias Corpus, an English dataset of expert-validated {\textless}audio, transcript{\textgreater} pairs with demographic tags for age, gender, accent. We also release open software which may be used with the Artie Bias Corpus to detect demographic bias in Automatic Speech Recognition systems, and can be extended to other speech technologies. The Artie Bias Corpus is a curated subset of the Mozilla Common Voice corpus, which we release under a Creative Commons CC0 license {--} the most open and permissive license for data. This article contains information on the criteria used to select and annotate the Artie Bias Corpus in addition to experiments in which we detect and attempt to mitigate bias in end-to-end speech recognition models. We we observe a significant accent bias in our baseline DeepSpeech model, with more accurate transcriptions of US English compared to Indian English. We do not, however, find evidence for a significant gender bias. We then show significant improvements on individual demographic groups from fine-tuning.
Evaluation of Off-the-shelf Speech Recognizers Across Diverse Dialogue Domains
Authors: Kallirroi Georgila, Anton Leuski, Volodymyr Yanov, David Traum
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We evaluate several publicly available off-the-shelf (commercial and research) automatic speech recognition (ASR) systems across diverse dialogue domains (in US-English). Our evaluation is aimed at non-experts with limited experience in speech recognition. Our goal is not only to compare a variety of ASR systems on several diverse data sets but also to measure how much ASR technology has advanced since our previous large-scale evaluations on the same data sets. Our results show that the performance of each speech recognizer can vary significantly depending on the domain. Furthermore, despite major recent progress in ASR technology, current state-of-the-art speech recognizers perform poorly in domains that require special vocabulary and language models, and under noisy conditions. We expect that our evaluation will prove useful to ASR consumers and dialogue system designers.
CEASR: A Corpus for Evaluating Automatic Speech Recognition
Authors: Malgorzata Anna Ulasik, Manuela H{\"u}rlimann, Fabian Germann, Esin Gedik, Fern Benites, o, Mark Cieliebak
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In this paper, we present CEASR, a Corpus for Evaluating the quality of Automatic Speech Recognition (ASR). It is a data set based on public speech corpora, containing metadata along with transcripts generated by several modern state-of-the-art ASR systems. CEASR provides this data in a unified structure, consistent across all corpora and systems, with normalised transcript texts and metadata. We use CEASR to evaluate the quality of ASR systems by calculating an average Word Error Rate (WER) per corpus, per system and per corpus-system pair. Our experiments show a substantial difference in accuracy between commercial versus open-source ASR tools as well as differences up to a factor ten for single systems on different corpora. Using CEASR allowed us to very efficiently and easily obtain these results. Our corpus enables researchers to perform ASR-related evaluations and various in-depth analyses with noticeably reduced effort, i.e. without the need to collect, process and transcribe the speech data themselves.
Open-source Multi-speaker Speech Corpora for Building Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu Speech Synthesis Systems
Authors: Fei He, Shan-Hui Cathy Chu, Oddur Kjartansson, Clara Rivera, Anna Katanova, Alex Gutkin, er, Isin Demirsahin, Cibu Johny, Martin Jansche, Supheakmungkol Sarin, Knot Pipatsrisawat
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Speech Synthesis, text-to-speech, Text to Speech

We present free high quality multi-speaker speech corpora for Gujarati, Kannada, Malayalam, Marathi, Tamil and Telugu, which are six of the twenty two official languages of India spoken by 374 million native speakers. The datasets are primarily intended for use in text-to-speech (TTS) applications, such as constructing multilingual voices or being used for speaker or language adaptation. Most of the corpora (apart from Marathi, which is a female-only database) consist of at least 2,000 recorded lines from female and male native speakers of the language. We present the methodological details behind corpora acquisition, which can be scaled to acquiring data for other languages of interest. We describe the experiments in building a multilingual text-to-speech model that is constructed by combining our corpora. Our results indicate that using these corpora results in good quality voices, with Mean Opinion Scores (MOS) {\textgreater} 3.6, for all the languages tested. We believe that these resources, released with an open-source license, and the described methodology will help in the progress of speech applications for the languages described and aid corpora development for other, smaller, languages of India and beyond.
Crowdsourcing Latin American Spanish for Low-Resource Text-to-Speech
Authors: Adriana Guevara-Rukoz, Isin Demirsahin, Fei He, Shan-Hui Cathy Chu, Supheakmungkol Sarin, Knot Pipatsrisawat, Alex Gutkin, er, Alena Butryna, Oddur Kjartansson
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: text-to-speech, Text to Speech

In this paper we present a multidialectal corpus approach for building a text-to-speech voice for a new dialect in a language with existing resources, focusing on various South American dialects of Spanish. We first present public speech datasets for Argentinian, Chilean, Colombian, Peruvian, Puerto Rican and Venezuelan Spanish specifically constructed with text-to-speech applications in mind using crowd-sourcing. We then compare the monodialectal voices built with minimal data to a multidialectal model built by pooling all the resources from all dialects. Our results show that the multidialectal model outperforms the monodialectal baseline models. We also experiment with a {``}zero-resource{''} dialect scenario where we build a multidialectal voice for a dialect while holding out target dialect recordings from the training data.
A Manually Annotated Resource for the Investigation of Nasal Grunts
Authors: Aur{\'e}lie Chl{\'e}bowski, Nicolas Ballier
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents an annotation framework for nasal grunts of the whole French CID corpus (Bertrand et al., 2008). The acoustic components under scrutiny are justified and the annotation guidelines are described. We carefully characterise the acoustic cues and visual cues followed by the annotator, especially for non-modal phonation types. The conventions followed for the annotation of interactional and positional properties of grunts are explained. The resulting datasets after data extraction with Praat scripts (Boersma and Weenink, 2019) are analysed with R (R Core Team, 2017), focusing on duration. We analyse the effect of non-modal phonation (especially ingressive phonation) on duration and discuss a specialisation of grunts observed in the CID for grunts with ingressive phonation. The more general aim of this research is to establish putative core and additive properties of grunts and a tentative typology of grunts in spoken interactions.
The Objective and Subjective Sleepiness Voice Corpora
Authors: Vincent P. Martin, Jean-Luc Rouas, Jean-Arthur Micoulaud Franchi, Pierre Philip
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Following patients with chronic sleep disorders involves multiple appointments between doctors and patients which often results in episodic follow-ups with unevenly spaced interviews. Speech technologies and virtual doctors can help improve this follow-up. However, there are still some challenges to overcome: sleepiness measurements are diverse and are not always correlated, and most past research focused on detecting nstantaneous sleepiness levels of healthy sleep-deprived subjects. This article presents a large database to assess the sleepiness level of highly phenotyped patients that complain from excessive daytime sleepiness. Based on the Multiple Sleep Latency Test, it differs from existing databases by multiple aspects. First, it is omposed of recordings from patients suffering from excessive daytime sleepiness instead of sleep deprived healthy subjects. Second, it incites the subjects to sleep contrary to existing stressing sleepiness deprivation experimental paradigms. Third, the sleepiness level of the patients is evaluated with different temporal granularities - long term sleepiness and short term sleepiness - and both objective and subjective sleepiness measures are collected. Finally, it relies on the recordings of 94 highly phenotyped patients, allowing to unravel the influences of different physical factors (age, sex, weight, ... ) on voice.
Open-source Multi-speaker Corpora of the English Accents in the British Isles
Authors: Isin Demirsahin, Oddur Kjartansson, Alex Gutkin, er, Clara Rivera
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

This paper presents a dataset of transcribed high-quality audio of English sentences recorded by volunteers speaking with different accents of the British Isles. The dataset is intended for linguistic analysis as well as use for speech technologies. The recording scripts were curated specifically for accent elicitation, covering a variety of phonological phenomena and providing a high phoneme coverage. The scripts include pronunciations of global locations, major airlines and common personal names in different accents; and native speaker pronunciations of local words. Overlapping lines for all speakers were included for idiolect elicitation, which include the same or similar lines with other existing resources such as the CSTR VCTK corpus and the Speech Accent Archive to allow for easy comparison of personal and regional accents. The resulting corpora include over 31 hours of recordings from 120 volunteers who self-identify as native speakers of Southern England, Midlands, Northern England, Welsh, Scottish and Irish varieties of English.
TV-AfD: An Imperative-Annotated Corpus from The Big Bang Theory and Wikipedia's Articles for Deletion Discussions
Authors: Yimin Xiao, Zong-Ying Slaton, Lu Xiao
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Articles, General Classification

In this study, we created an imperative corpus with speech conversations from dialogues in The Big Bang Theory and with the written comments in Wikipedia{'}s Articles for Deletion discussions. For the TV show data, 59 episodes containing 25,076 statements are used. We manually annotated imperatives based on the annotation guideline adapted from Condoravdi and Lauer{'}s study (2012) and used the retrieved data to assess the performance of syntax-based classification rules. For the Wikipedia AfD comments data, we first developed and leveraged a syntax-based classifier to extract 10,624 statements that may be imperative, and we manually examined the statements and then identified true positives. With this corpus, we also examined the performance of the rule-based imperative detection tool. Our result shows different outcomes for speech (dialogue) and written data. The rule-based classification performs better in the written data in precision (0.80) compared to the speech data (0.44). Also, the rule-based classification has a low-performance overall for speech data with the precision of 0.44, recall of 0.41, and f-1 measure of 0.42. This finding implies the syntax-based model may need to be adjusted for a speech dataset because imperatives in oral communication have greater syntactic varieties and are highly context-dependent.
A Large Scale Speech Sentiment Corpus
Authors: Eric Chen, Zhiyun Lu, Hao Xu, Liangliang Cao, Yu Zhang, James Fan
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentiment Analysis

We present a multimodal corpus for sentiment analysis based on the existing Switchboard-1 Telephone Speech Corpus released by the Linguistic Data Consortium. This corpus extends the Switchboard-1 Telephone Speech Corpus by adding sentiment labels from 3 different human annotators for every transcript segment. Each sentiment label can be one of three options: positive, negative, and neutral. Annotators are recruited using Google Cloud{'}s data labeling service and the labeling task was conducted over the internet. The corpus contains a total of 49500 labeled speech segments covering 140 hours of audio. To the best of our knowledge, this is the largest multimodal Corpus for sentiment analysis that includes both speech and text features.
SibLing Corpus of Russian Dialogue Speech Designed for Research on Speech Entrainment
Authors: Tatiana Kachkovskaia, Tatiana Chukaeva, Vera Evdokimova, Pavel Kholiavin, Natalia Kriakina, Daniil Kocharov, Anna Mamushina, Alla Menshikova, Svetlana Zimina
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

The paper presents a new corpus of dialogue speech designed specifically for research in the field of speech entrainment. Given that the degree of accommodation may depend on a number of social factors, the corpus is designed to encompass 5 types of relations between the interlocutors: those between siblings, close friends, strangers of the same gender, strangers of the other gender, strangers of which one has a higher job position and greater age. Another critical decision taken in this corpus is that in all these social settings one speaker is kept the same. This allows us to trace the changes in his/her speech depending on the interlocutor. The basic set of speakers consists of 10 pairs of same-gender siblings (including 4 pairs of identical twins) aged 23-40, and each of them was recorded in the 5 settings mentioned above. In total we obtained 90 dialogues of 25-60 minutes each. The speakers played a card game and a map game; they were recorded in a soundproof studio without being able to see each other due to a non-transparent screen between them. The corpus contains orthographic, phonetic and prosodic annotation and is segmented into turns and inter-pausal units.
SMASH Corpus: A Spontaneous Speech Corpus Recording Third-person Audio Commentaries on Gameplay
Authors: Yuki Saito, Shinnosuke Takamichi, Hiroshi Saruwatari
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5

Developing a spontaneous speech corpus would be beneficial for spoken language processing and understanding. We present a speech corpus named the SMASH corpus, which includes spontaneous speech of two Japanese male commentators that made third-person audio commentaries during the gameplay of a fighting game. Each commentator ad-libbed while watching the gameplay with various topics covering not only explanations of each moment to convey the information on the fight but also comments to entertain listeners. We made transcriptions and topic tags as annotations on the recorded commentaries with our two-step method. We first made automatic and manual transcriptions of the commentaries and then manually annotated the topic tags. This paper describes how we constructed the SMASH corpus and reports some results of the annotations.
Improving Speech Recognition for the Elderly: A New Corpus of Elderly Japanese Speech and Investigation of Acoustic Modeling for Speech Recognition
Authors: Meiko Fukuda, Hiromitsu Nishizaki, Yurie Iribe, Ryota Nishimura, Norihide Kitaoka
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: speech-recognition, Speech Recognition

In an aging society like Japan, a highly accurate speech recognition system is needed for use in electronic devices for the elderly, but this level of accuracy cannot be obtained using conventional speech recognition systems due to the unique features of the speech of elderly people. S-JNAS, a corpus of elderly Japanese speech, is widely used for acoustic modeling in Japan, but the average age of its speakers is 67.6 years old. Since average life expectancy in Japan is now 84.2 years, we are constructing a new speech corpus, which currently consists of the utterances of 221 speakers with an average age of 79.2, collected from four regions of Japan. In addition, we expand on our previous study (Fukuda, 2019) by further investigating the construction of acoustic models suitable for elderly speech. We create new acoustic models and train them using a combination of existing Japanese speech corpora (JNAS, S-JNAS, CSJ), with and without our {`}super-elderly{'} speech data, and conduct speech recognition experiments. Our new acoustic models achieve word error rates (WER) as low as 13.38{\%}, exceeding the results of our previous study in which we used the CSJ acoustic model adapted for elderly speech (17.4{\%} WER).
Preparation of Bangla Speech Corpus from Publicly Available Audio \& Text
Authors: Shafayat Ahmed, Nafis Sadeq, Sudipta Saha Shubha, Md. Nahidul Islam, Muhammad Abdullah Adnan, Mohammad Zuberul Islam
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition

Automatic speech recognition systems require large annotated speech corpus. The manual annotation of a large corpus is very difficult. In this paper, we focus on the automatic preparation of a speech corpus for Bangladeshi Bangla. We have used publicly available Bangla audiobooks and TV news recordings as audio sources. We designed and implemented an iterative algorithm that takes as input a speech corpus and a huge amount of raw audio (without transcription) and outputs a much larger speech corpus with reasonable confidence. We have leveraged speaker diarization, gender detection, etc. to prepare the annotated corpus. We also have prepared a synthetic speech corpus for handling out-of-vocabulary word problems in Bangla language. Our corpus is suitable for training with Kaldi. Experimental results show that the use of our corpus in addition to the Google Speech corpus (229 hours) significantly improves the performance of the ASR system.
On Construction of the ASR-oriented Indian English Pronunciation Dictionary
Authors: Xian Huang, Xin Jin, Qike Li, Keliang Zhang
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modelling, speech-recognition, Speech Recognition

As a World English, a New English and a regional variety of English, Indian English (IE) has developed its own distinctive characteristics, especially phonologically, from other varieties of English. An Automatic Speech Recognition (ASR) system simply trained on British English (BE) /American English (AE) speech data and using the BE/AE pronunciation dictionary performs much worse when applied to IE. An applicable IEASR system needs spontaneous IE speech as training materials and a comprehensive, linguistically-guided IE pronunciation dictionary (IEPD) so as to achieve the effective mapping between the acoustic model and language model. This research builds a small IE spontaneous speech corpus, analyzes and summarizes the phonological variation features of IE, comes up with an IE phoneme set and complies the IEPD (including a common-English-word list, an Indian-word list, an acronym list and an affix list). Finally, two ASR systems are trained with 120 hours IE spontaneous speech data, using the IEPD we construct in this study and CMUdict separately. The two systems are tested with 50 audio clips of IE spontaneous speech. The result shows the system trained with IEPD performs better than the one trained with CMUdict with WER being 15.63{\%} lower on the test data.
RSC: A Romanian Read Speech Corpus for Automatic Speech Recognition
Authors: Alex Georgescu, ru-Lucian, Horia Cucu, Andi Buzo, Corneliu Burileanu
Date: 2020-05-01 00:00:00
Proceeding: LREC 2020 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Although many efforts have been made in the last decade to enhance the speech and language resources for Romanian, this language is still considered under-resourced. While for many other languages there are large speech corpora available for research and commercial applications, for Romanian language the largest publicly available corpus to date comprises less than 50 hours of speech. In this context, Speech and Dialogue research group releases Read Speech Corpus (RSC) {--} a Romanian speech corpus developed in-house, comprising 100 hours of speech recordings from 164 different speakers. The paper describes the development of the corpus and presents baseline automatic speech recognition (ASR) results using state-of-the-art ASR technology: Kaldi speech recognition toolkit.