scholarly journals The role of binary mask patterns in automatic speech recognition in background noise

2013 ◽  
Vol 133 (5) ◽  
pp. 3083-3093 ◽  
Author(s):  
Arun Narayanan ◽  
DeLiang Wang
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Background Noise ◽  
Binary Mask

Multi-channel non-negative matrix factorization with binary mask initialization for automatic speech recognition

2016 ◽  
Vol 140 (4) ◽  
pp. 3450-3450
Author(s):  
Iori Miura ◽  
Yuuki Tachioka ◽  
Tomohiro Narita ◽  
Jun Ishii ◽  
Fuminori Yoshiyama ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Matrix Factorization ◽  
Binary Mask ◽  
Non Negative Matrix Factorization

scholarly journals A Hybrid Speech Enhancement Algorithm for Voice Assistance Application

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7025
Author(s):  
Jenifa Gnanamanickam ◽  
Yuvaraj Natarajan ◽  
Sri Preethaa K. R.
Keyword(s):  
Speech Recognition ◽  
Speech Enhancement ◽  
Automatic Speech Recognition ◽  
Background Noise ◽  
Communication Systems ◽  
Spectral Subtraction ◽  
Channel Noise ◽  
Non Linear ◽  
Audio Files ◽  
Enhancement Algorithm

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.

Studying the role of pitch-adaptive spectral estimation and speaking-rate normalization in automatic speech recognition

2018 ◽  
Vol 79 ◽  
pp. 142-151 ◽  
Author(s):  
S. Shahnawazuddin ◽  
Nagaraj Adiga ◽  
Hemant K. Kathania ◽  
Gaydhar Pradhan ◽  
Rohit Sinha
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Spectral Estimation ◽  
Speaking Rate

Selecting the Optimal FM System for Children With Cochlear Implants

2008 ◽  
Vol 18 (1) ◽  
pp. 19-24
Author(s):  
Erin C. Schafer
Keyword(s):  
Speech Recognition ◽  
Cochlear Implants ◽  
Empirical Research ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Evidence Based ◽  
Signal To Noise ◽  
Speech Processor ◽  
System Input ◽  
Optimal Type

Children who use cochlear implants experience significant difficulty hearing speech in the presence of background noise, such as in the classroom. To address these difficulties, audiologists often recommend frequency-modulated (FM) systems for children with cochlear implants. The purpose of this article is to examine current empirical research in the area of FM systems and cochlear implants. Discussion topics will include selecting the optimal type of FM receiver, benefits of binaural FM-system input, importance of DAI receiver-gain settings, and effects of speech-processor programming on speech recognition. FM systems significantly improve the signal-to-noise ratio at the child's ear through the use of three types of FM receivers: mounted speakers, desktop speakers, or direct-audio input (DAI). This discussion will aid audiologists in making evidence-based recommendations for children using cochlear implants and FM systems.

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