Speech Recognition Based on Open Source Speech Processing Software

Abstract Considering the dependance of materials’ properties on the microstructure, it is imperative to carry out a thorough microstructural characterization and analysis to bolster its development. This article is aimed to inform the users about the implementation of FIJI, an open source image processing software for image segmentation and quantitative microstructural analysis. The rapid advancement of computer technology in the past years has made it possible to swiftly segment and analyze hundreds of micrographs reducing hours’ worth of analysis time to a mere matter of minutes. This has led to the availability of several commercial image processing software programs primarily aimed at relatively inexperienced users. Despite the advantages like ‘one-click solutions’ offered by commercial software, the high licensing cost limits its widespread use in the metallographic community. Open-source platforms on the other hand, are free and easily available although rudimentary knowledge of the user-interface is a pre-requisite. In particular, the software FIJI has distinguished itself as a versatile tool, since it provides suitable extensions from image processing to segmentation to quantitative stereology and is continuously developed by a large user community. This article aims to introduce the FIJI program by familiarizing the user with its graphical user-interface and providing a sequential methodology to carry out image segmentation and quantitative microstructural analysis.

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Automatic Speech Recognition Predicts Speech Intelligibility and Comprehension for Listeners With Simulated Age-Related Hearing Loss

Journal of Speech Language and Hearing Research ◽

10.1044/2017_jslhr-s-16-0269 ◽

2017 ◽

Vol 60 (9) ◽

pp. 2394-2405 ◽

Cited By ~ 6

Author(s):

Lionel Fontan ◽

Isabelle Ferrané ◽

Jérôme Farinas ◽

Julien Pinquier ◽

Julien Tardieu ◽

...

Keyword(s):

Hearing Loss ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Hearing Aids ◽

Speech Processing ◽

Fine Tuning ◽

Language Models ◽

Age Related ◽

Age Related Hearing Loss ◽

Asr System

Purpose The purpose of this article is to assess speech processing for listeners with simulated age-related hearing loss (ARHL) and to investigate whether the observed performance can be replicated using an automatic speech recognition (ASR) system. The long-term goal of this research is to develop a system that will assist audiologists/hearing-aid dispensers in the fine-tuning of hearing aids. Method Sixty young participants with normal hearing listened to speech materials mimicking the perceptual consequences of ARHL at different levels of severity. Two intelligibility tests (repetition of words and sentences) and 1 comprehension test (responding to oral commands by moving virtual objects) were administered. Several language models were developed and used by the ASR system in order to fit human performances. Results Strong significant positive correlations were observed between human and ASR scores, with coefficients up to .99. However, the spectral smearing used to simulate losses in frequency selectivity caused larger declines in ASR performance than in human performance. Conclusion Both intelligibility and comprehension scores for listeners with simulated ARHL are highly correlated with the performances of an ASR-based system. In the future, it needs to be determined if the ASR system is similarly successful in predicting speech processing in noise and by older people with ARHL.

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Monaural multi-talker speech recognition using factorial speech processing models

Speech Communication ◽

10.1016/j.specom.2018.01.007 ◽

2018 ◽

Vol 98 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Mahdi Khademian ◽

Mohammad Mehdi Homayounpour

Keyword(s):

Speech Recognition ◽

Speech Processing

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Open-source speech-processing platforms: An application example

The Journal of the Acoustical Society of America ◽

10.1121/1.5035672 ◽

2018 ◽

Vol 143 (3) ◽

pp. 1738-1738

Author(s):

Arthur Boothroyd ◽

Harinath Garudadri ◽

Gregory Hobbs

Keyword(s):

Open Source ◽

Speech Processing

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AISHELL-1: An open-source Mandarin speech corpus and a speech recognition baseline

2017 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA) ◽

10.1109/icsda.2017.8384449 ◽

2017 ◽

Cited By ~ 39

Author(s):

Hui Bu ◽

Jiayu Du ◽

Xingyu Na ◽

Bengu Wu ◽

Hao Zheng

Keyword(s):

Speech Recognition ◽

Open Source ◽

Speech Corpus

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Efficacy of a Cochlear Implant Simultaneous Analog Stimulation Strategy Coupled with a Monopolar Electrode Configuration

Annals of Otology Rhinology & Laryngology ◽

10.1177/000348940511401113 ◽

2005 ◽

Vol 114 (11) ◽

pp. 886-893 ◽

Cited By ~ 8

Author(s):

Li Xu ◽

Teresa A. Zwolan ◽

Catherine S. Thompson ◽

Bryan E. Pfingst

Keyword(s):

Speech Recognition ◽

Cochlear Implant ◽

Speech Processing ◽

Dynamic Range ◽

Recognition Performance ◽

Electrode Configuration ◽

Processing Strategy ◽

Open Set ◽

Hearing In Noise ◽

Noise Test

Objectives: The present study was performed to evaluate the efficacy and clinical feasibility of using monopolar stimulation with the Clarion Simultaneous Analog Stimulation (SAS) strategy in patients with cochlear implants. Methods: Speech recognition by 10 Clarion cochlear implant users was evaluated by means of 4 different speech processing strategy/electrode configuration combinations; ie, SAS and Continuous Interleaved Sampling (CIS) strategies were each used with monopolar (MP) and bipolar (BP) electrode configurations. The test measures included consonants, vowels, consonant-nucleus-consonant words, and Hearing in Noise Test sentences with a +10 dB signal-to-noise ratio. Additionally, subjective judgments of sound quality were obtained for each strategy/configuration combination. Results: All subjects but 1 demonstrated open-set speech recognition with the SAS/MP combination. The group mean Hearing in Noise Test sentence score for the SAS/MP combination was 31.6% (range, 0% to 92%) correct, as compared to 25.0%, 46.7%, and 37.8% correct for the CIS/BP, CIS/MP, and SAS/BP combinations, respectively. Intersubject variability was high, and there were no significant differences in mean speech recognition scores or mean preference ratings among the 4 strategy/configuration combinations tested. Individually, the best speech recognition performance was with the subject's everyday strategy/configuration combination in 72% of the applicable cases. If the everyday strategy was excluded from the analysis, the subjects performed best with the SAS/MP combination in 37.5% of the remaining cases. Conclusions: The SAS processing strategy with an MP electrode configuration gave reasonable speech recognition in most subjects, even though subjects had minimal previous experience with this strategy/configuration combination. The SAS/MP combination might be particularly appropriate for patients for whom a full dynamic range of electrical hearing could not be achieved with a BP configuration.

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