Mark Hasegawa-Johnson


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Context-Aware Automatic Text Simplification of Health Materials in Low-Resource Domains
Tarek Sakakini | Jong Yoon Lee | Aditya Duri | Renato F.L. Azevedo | Victor Sadauskas | Kuangxiao Gu | Suma Bhat | Dan Morrow | James Graumlich | Saqib Walayat | Mark Hasegawa-Johnson | Thomas Huang | Ann Willemsen-Dunlap | Donald Halpin
Proceedings of the 11th International Workshop on Health Text Mining and Information Analysis

Healthcare systems have increased patients’ exposure to their own health materials to enhance patients’ health levels, but this has been impeded by patients’ lack of understanding of their health material. We address potential barriers to their comprehension by developing a context-aware text simplification system for health material. Given the scarcity of annotated parallel corpora in healthcare domains, we design our system to be independent of a parallel corpus, complementing the availability of data-driven neural methods when such corpora are available. Our system compensates for the lack of direct supervision using a biomedical lexical database: Unified Medical Language System (UMLS). Compared to a competitive prior approach that uses a tool for identifying biomedical concepts and a consumer-directed vocabulary list, we empirically show the enhanced accuracy of our system due to improved handling of ambiguous terms. We also show the enhanced accuracy of our system over directly-supervised neural methods in this low-resource setting. Finally, we show the direct impact of our system on laypeople’s comprehension of health material via a human subjects’ study (n=160).


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Clustering-based Phonetic Projection in Mismatched Crowdsourcing Channels for Low-resourced ASR
Wenda Chen | Mark Hasegawa-Johnson | Nancy Chen | Preethi Jyothi | Lav Varshney
Proceedings of the 6th Workshop on South and Southeast Asian Natural Language Processing (WSSANLP2016)

Acquiring labeled speech for low-resource languages is a difficult task in the absence of native speakers of the language. One solution to this problem involves collecting speech transcriptions from crowd workers who are foreign or non-native speakers of a given target language. From these mismatched transcriptions, one can derive probabilistic phone transcriptions that are defined over the set of all target language phones using a noisy channel model. This paper extends prior work on deriving probabilistic transcriptions (PTs) from mismatched transcriptions by 1) modelling multilingual channels and 2) introducing a clustering-based phonetic mapping technique to improve the quality of PTs. Mismatched crowdsourcing for multilingual channels has certain properties of projection mapping, e.g., it can be interpreted as a clustering based on singular value decomposition of the segment alignments. To this end, we explore the use of distinctive feature weights, lexical tone confusions, and a two-step clustering algorithm to learn projections of phoneme segments from mismatched multilingual transcriber languages to the target language. We evaluate our techniques using mismatched transcriptions for Cantonese speech acquired from native English and Mandarin speakers. We observe a 5-9% relative reduction in phone error rate for the predicted Cantonese phone transcriptions using our proposed techniques compared with the previous PT method.


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A PAC-Bayesian Approach to Minimum Perplexity Language Modeling
Sujeeth Bharadwaj | Mark Hasegawa-Johnson
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

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Development of a TV Broadcasts Speech Recognition System for Qatari Arabic
Mohamed Elmahdy | Mark Hasegawa-Johnson | Eiman Mustafawi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

A major problem with dialectal Arabic speech recognition is due to the sparsity of speech resources. In this paper, a transfer learning framework is proposed to jointly use a large amount of Modern Standard Arabic (MSA) data and little amount of dialectal Arabic data to improve acoustic and language modeling. The Qatari Arabic (QA) dialect has been chosen as a typical example for an under-resourced Arabic dialect. A wide-band speech corpus has been collected and transcribed from several Qatari TV series and talk-show programs. A large vocabulary speech recognition baseline system was built using the QA corpus. The proposed MSA-based transfer learning technique was performed by applying orthographic normalization, phone mapping, data pooling, acoustic model adaptation, and system combination. The proposed approach can achieve more than 28% relative reduction in WER.

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Automatic Long Audio Alignment and Confidence Scoring for Conversational Arabic Speech
Mohamed Elmahdy | Mark Hasegawa-Johnson | Eiman Mustafawi
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In this paper, a framework for long audio alignment for conversational Arabic speech is proposed. Accurate alignments help in many speech processing tasks such as audio indexing, speech recognizer acoustic model (AM) training, audio summarizing and retrieving, etc. We have collected more than 1,400 hours of conversational Arabic besides the corresponding human generated non-aligned transcriptions. Automatic audio segmentation is performed using a split and merge approach. A biased language model (LM) is trained using the corresponding text after a pre-processing stage. Because of the dominance of non-standard Arabic in conversational speech, a graphemic pronunciation model (PM) is utilized. The proposed alignment approach is performed in two passes. Firstly, a generic standard Arabic AM is used along with the biased LM and the graphemic PM in a fast speech recognition pass. In a second pass, a more restricted LM is generated for each audio segment, and unsupervised acoustic model adaptation is applied. The recognizer output is aligned with the processed transcriptions using Levenshtein algorithm. The proposed approach resulted in an initial alignment accuracy of 97.8-99.0% depending on the amount of disfluencies. A confidence scoring metric is proposed to accept/reject aligner output. Using confidence scores, it was possible to reject the majority of mis-aligned segments resulting in alignment accuracy of 99.0-99.8% depending on the speech domain and the amount of disfluencies.


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Detection of Acoustic-Phonetic Landmarks in Mismatched Conditions using a Biomimetic Model of Human Auditory Processing
Sarah King | Mark Hasegawa-Johnson
Proceedings of COLING 2012: Posters


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State-Transition Interpolation and MAP Adaptation for HMM-based Dysarthric Speech Recognition
Harsh Vardhan Sharma | Mark Hasegawa-Johnson
Proceedings of the NAACL HLT 2010 Workshop on Speech and Language Processing for Assistive Technologies


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Speech Retrieval in Unknown Languages: a Pilot Study
Xiaodan Zhuang | Jui Ting Huang | Mark Hasegawa-Johnson
Proceedings of the Third International Workshop on Cross Lingual Information Access: Addressing the Information Need of Multilingual Societies (CLIAWS3)

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On Semi-Supervised Learning of Gaussian Mixture Models for Phonetic Classification
Jui-Ting Huang | Mark Hasegawa-Johnson
Proceedings of the NAACL HLT 2009 Workshop on Semi-supervised Learning for Natural Language Processing


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Speech Recognition Models of the Interdependence Among Syntax, Prosody, and Segmental Acoustics
Mark Hasegawa-Johnson | Jennifer Cole | Chilin Shih | Ken Chen | Aaron Cohen | Sandra Chavarria | Heejin Kim | Taejin Yoon | Sarah Borys | Jeung-Yoon Choi
Proceedings of the HLT-NAACL 2004 Workshop on Spoken Language Understanding for Conversational Systems and Higher Level Linguistic Information for Speech Processing