NDLI: Automatic speech recognition for mixed dialect utterances by mixing dialect language models

Content Provider	ACM Digital Library
Author	Yoshino, Koichiro Mori, Shinsuke Hirayama, Naoki Itoyama, Katsutoshi Okuno, Hiroshi G.
Copyright Year	2015
Abstract	This paper presents an automatic speech recognition (ASR) system that accepts a mixture of various kinds of dialects. The system recognizes dialect utterances on the basis of the statistical simulation of vocabulary transformation and combinations of several dialect models. Previous dialect ASR systems were based on handcrafted dictionaries for several dialects, which involved costly processes. The proposed system statistically trains transformation rules between a common language and dialects, and simulates a dialect corpus for ASR on the basis of a machine translation technique. The rules are trained with small sets of parallel corpora to make up for the lack of linguistic resources on dialects. The proposed system also accepts mixed dialect utterances that contain a variety of vocabularies. In fact, spoken language is not a single dialect but a mixed dialect that is affected by the circumstances of speakers' backgrounds (e.g., native dialects of their parents or where they live). We addressed two methods to combine several dialects appropriately for each speaker. The first was recognition with language models of mixed dialects with automatically estimated weights that maximized the recognition likelihood. This method performed the best, but calculation was very expensive because it conducted grid searches of combinations of dialect mixing proportions that maximized the recognition likelihood. The second was integration of results of recognition from each single dialect language model. The improvements with this model were slightly smaller than those with the first method. Its calculation cost was, however, inexpensive and it worked in real-time on general workstations. Both methods achieved higher recognition accuracies for all speakers than those with the single dialect models and the common language model, and we could choose a suitable model for use in ASR that took into consideration the computational costs and recognition accuracies.
Starting Page	373
Ending Page	382
Page Count	10
File Format	PDF
ISSN	23299290
e-ISSN	23299304
DOI	10.1109/TASLP.2014.2387414
Volume Number	23
Issue Number	2
Journal	IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-02-01
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Corpus simulation Mixture of dialects Speech recognition
Content Type	Text
Resource Type	Article
Subject	Instrumentation Computational Mathematics Signal Processing Electrical and Electronic Engineering Acoustics and Ultrasonics Speech and Hearing Media Technology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automatic Speech Recognition for Mixed Dialect Utterances by Mixing Dialect Language Models

Characterizing Phonetic Transformations and Acoustic Differences Across English Dialects

Non-negative factor analysis of Gaussian mixture model weight adaptation for language and dialect recognition

Finding Complex Features for Guest Language Fragment Recovery in Resource-Limited Code-Mixed Speech Recognition

Finding complex features for guest language fragment recovery in resource-limited code-mixed speech recognition

Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition

i-Vector Modeling of Speech Attributes for Automatic Foreign Accent Recognition

Non-Negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition

Cross-Lingual Subspace Gaussian Mixture Models for Low-Resource Speech Recognition

Automatic speech recognition for mixed dialect utterances by mixing dialect language models

Similar Documents

Automatic Speech Recognition for Mixed Dialect Utterances by Mixing Dialect Language Models

Characterizing Phonetic Transformations and Acoustic Differences Across English Dialects

Non-negative factor analysis of Gaussian mixture model weight adaptation for language and dialect recognition

Finding Complex Features for Guest Language Fragment Recovery in Resource-Limited Code-Mixed Speech Recognition

Finding complex features for guest language fragment recovery in resource-limited code-mixed speech recognition

Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition

i-Vector Modeling of Speech Attributes for Automatic Foreign Accent Recognition

Non-Negative Factor Analysis of Gaussian Mixture Model Weight Adaptation for Language and Dialect Recognition

Cross-Lingual Subspace Gaussian Mixture Models for Low-Resource Speech Recognition

Automatic speech recognition for mixed dialect utterances by mixing dialect language models