Recognition of Children Punjabi Speech using Tonal Non-Tonal Classifier

Hasija, Taniya; Kadyan, Virender; Guleria, Kalpna

doi:10.1109/esci50559.2021.9397041

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…Various prosodic features have been extracted in the past to address an ASR system's low efficiency. The prosodic features are F0, voicing probability, intensity, loudness, voice quality, harmonic-to-noise ratio (HNR), F0 raw, and F0 envelope [11]. An autocorrelation technique extracts pitch predictions from the input speech signal [38].…”

Section: Prosodic Featuresmentioning

confidence: 99%

“…Then, on F0 raw, pitch trimming and smoothing methods are applied, and F0 is obtained. Later, the pitch means subtraction algorithm is added to the F0 function, and the probability of voicing is captured, as it indicates the percentage of the signal's unvoiced and voiced data [11]. Let the normalized cross-correlation function value be 'a', which must be absolute on a particular frame.…”

Section: Prosodic Featuresmentioning

confidence: 99%

“…These prosodic features also capture psychological qualities, speaking style, and inter-speaker variances, whereas traditional feature-extraction algorithms capture phonetic components of a speech signal. Pitch features are essential in extracting the tonal aspects of a languages' characteristics [11].…”

Section: Introductionmentioning

confidence: 99%

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Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System

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Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.

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Section: Prosodic Featuresmentioning

confidence: 99%

Section: Prosodic Featuresmentioning

confidence: 99%