Signal Processing Cues to Improve Automatic Speech Recognition for Low Resource Indian Languages

Methods based on artificial neural networks (ANN) are widely used in various audio signal processing tasks. This provides opportunities to optimize processes and save resources required for calculations. One of the main objects we need to get to numerically capture the acoustics of a room is the room impulse response (RIR). Increasingly, research authors choose not to record these impulses in a real room but to generate them using ANN, as this gives them the freedom to prepare unlimited-sized training datasets. Neural networks are also used to augment the generated impulses to make them similar to the ones actually recorded. The widest use of ANN so far is observed in the evaluation of the generated results, for example, in automatic speech recognition (ASR) tasks. This review also describes datasets of recorded RIR impulses commonly found in various studies that are used as training data for neural networks.

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Evaluating microphones and microphone placement for signal processing and automatic speech recognition of teacher-student dialog

The Journal of the Acoustical Society of America ◽

10.1121/1.4900042 ◽

2014 ◽

Vol 136 (4) ◽

pp. 2215-2215 ◽

Cited By ~ 1

Author(s):

Michael C. Brady ◽

Sydney D'Mello ◽

Nathan Blanchard ◽

Andrew Olney ◽

Martin Nystrand

Keyword(s):

Signal Processing ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Teacher Student

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Introduction to Speech Recognition

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch007 ◽

2011 ◽

pp. 141-161

Author(s):

Sergio Suárez-Guerra ◽

Jose Luis Oropeza-Rodriguez

Keyword(s):

Artificial Intelligence ◽

Signal Processing ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Speech Signal ◽

Essential Information ◽

Science Field ◽

Applied Artificial Intelligence ◽

And Linguistics ◽

Successful Technology

This chapter presents the state-of-the-art automatic speech recognition (ASR) technology, which is a very successful technology in the computer science field, related to multiple disciplines such as the signal processing and analysis, mathematical statistics, applied artificial intelligence and linguistics, and so forth. The unit of essential information used to characterize the speech signal in the most widely used ASR systems is the phoneme. However, recently several researchers have questioned this representation and demonstrated the limitations of the phonemes, suggesting that ASR with better performance can be developed replacing the phoneme by triphones and syllables as the unit of essential information used to characterize the speech signal. This chapter presents an overview of the most successful techniques used in ASR systems together with some recently proposed ASR systems that intend to improve the characteristics of conventional ASR systems.

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Introduction to Speech Recognition

Advances in Audio and Speech Signal Processing ◽

10.4018/978-1-59904-132-2.ch011 ◽

2011 ◽

pp. 325-348 ◽

Cited By ~ 2

Author(s):

Sergio Suárez-Guerra ◽

Jose Luis Oropeza-Rodriguez

Keyword(s):

Artificial Intelligence ◽

Signal Processing ◽

Speech Recognition ◽

Automatic Speech Recognition ◽

Speech Signal ◽

Essential Information ◽

Science Field ◽

Applied Artificial Intelligence ◽

And Linguistics ◽

Successful Technology

This chapter presents the state-of-the-art automatic speech recognition (ASR) technology, which is a very successful technology in the computer science field, related to multiple disciplines such as the signal processing and analysis, mathematical statistics, applied artificial intelligence and linguistics, and so forth. The unit of essential information used to characterize the speech signal in the most widely used ASR systems is the phoneme. However, recently several researchers have questioned this representation and demonstrated the limitations of the phonemes, suggesting that ASR with better performance can be developed replacing the phoneme by triphones and syllables as the unit of essential information used to characterize the speech signal. This chapter presents an overview of the most successful techniques used in ASR systems together with some recently proposed ASR systems that intend to improve the characteristics of conventional ASR systems.

Download Full-text