Data-Driven and Feedback Based Spectro-Temporal Features for Speech Recognition

Sivaram, G.S.V.S.; Nemala, Sridhar Krishna; Mesgarani, Nima; Heřmanský, Hynek

doi:10.1109/lsp.2010.2079930

Cited by 14 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This joint spectrotemporal analysis effectively performs a double wavelet transform of the auditory spectrogram revealing and capturing efficiently the distinctive patterns of modulations of speech, music, and other complex environmental sounds. For example, “cortical features” have been shown to be effective in numerous applications ranging from speech intelligibility assessment [26], to hearing impairments and hearing aids, to the detection of speech, speaker identification, speech recognition [27,28], and musical timbre classification [29]. Most enlightening, however, have been the mathematical analyses of closely related constructs employing 'cascaded wavelet transforms' that have explained the reasons underlying the effectiveness of these cortical features [30].…”

Section: Primary Auditory Cortical Processing and Representationsmentioning

confidence: 99%

Adaptive auditory computations

Shamma

Fritz

2014

Current Opinion in Neurobiology

View full text Add to dashboard Cite

The auditory system analyses acoustic signals, extracting their perceptual attributes, and exploiting them to navigate complex auditory environments. While many of the basic transformations that give rise to the early auditory representations are well studied and understood, little is known about the latter cognitive functions that bind, organize, and give meaning to them. They include the ability to attend to, segregate, and track one of many sound sources, to learn its identity, commit it to memory, robustly recognize it, and utilize it to make decisions. This review hints at the profound adaptive influences and contextual effects induced by cognitive functions during these behaviors, and the need for robust tractable mathematical models to understand them.

show abstract

Section: Primary Auditory Cortical Processing and Representationsmentioning

confidence: 99%

Adaptive auditory computations

Shamma

Fritz

2014

Current Opinion in Neurobiology

View full text Add to dashboard Cite

show abstract

“…In most of the research on speech processing, the Fourier transform and its variants are used to convert the speech from the time domain into the frequency domain. With this approach, some well known speech features were introduced such as spectral, cepstral [2] [3] [4], Mel cepstral [5] [6], MFCC [7], [8]. Because the human brain understands the properties of speech in the frequency domain, these features are widely used and gain many possible results.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Signal Linear Trend in the Time Domain to Enhance Speech Feature

Duc

2022

IEEE Access

View full text Add to dashboard Cite

Speech feature extraction usually begins with transforming the signal from the time domain to the frequency domain via integral transforms. Human speech contains sine-shape waves and a linear trend in the time domain, usually ignored. This research proposes two new methods to strengthen the speech feature vector using this unused factor. Before transforming the speech frames into the frequency domain, we use linear regression to identify each speech frame's linear trend or linear envelope in the time domain. Then we remove the impact of that trend to normalize the signal and emphasize the stationary elements in the signal. The proposed feature vector includes parameters from the linear envelope and the conventional vectors as a spectrum or MFCC. Experimental results demonstrate that the impact of the linear envelope is significant and the linear envelope subtraction is a meaningful stage. Our new features emphasize the stationary property in the speech signal and improve the result for speech recognition in terms of error rate.

show abstract

“…Consequently, some of the research works have considered one of the South Asian languages like Marathi. However, there is no evidence for o ering e ective solutions while recognizing the Marathi language [3]. Moreover, the Marathi language model suffers from inadequate SC and small size vocabulary systems [4].…”

Section: Introductionmentioning

confidence: 99%

Parallel Big Bang-Big Crunch-LSTM Approach for Developing a Marathi Speech Recognition System

Sharma

Bachate

Singh

et al. 2022

Mobile Information Systems

View full text Add to dashboard Cite

The Voice User Interface (VUI) for human-computer interaction has received wide acceptance, due to which the systems for speech recognition in regional languages are now being developed, taking into account all of the dialects. Because of the limited availability of the speech corpus (SC) of regional languages for doing research, designing a speech recognition system is challenging. This contribution provides a Parallel Big Bang-Big Crunch (PB3C)-based mechanism to automatically evolve the optimal architecture of LSTM (Long Short-Term Memory). To decide the optimal architecture, we evolved a number of neurons and hidden layers of LSTM model. We validated the proposed approach on Marathi speech recognition system. In this research work, the performance comparisons of the proposed method are done with BBBC based LSTM and manually configured LSTM. The results indicate that the proposed approach is better than two other approaches.

show abstract

Data-Driven and Feedback Based Spectro-Temporal Features for Speech Recognition

Cited by 14 publications

References 16 publications

Adaptive auditory computations

Adaptive auditory computations

Exploiting Signal Linear Trend in the Time Domain to Enhance Speech Feature

Parallel Big Bang-Big Crunch-LSTM Approach for Developing a Marathi Speech Recognition System

Contact Info

Product

Resources

About