Performance Analysis of Feature sets in Speaker Diarization techniques

Sailaja, C.; Maloji, Suman; Manepalli, Kasiprasad

doi:10.1088/1742-6596/1804/1/012166

Cited by 1 publication

(1 citation statement)

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“…Yu et al [22] extended the deep LSTM RNN and achieved excellent performance in long-range speech recognition tasks. Sailaja et al used LSTM-RNN as a classification model to process features extracted using MFCC and spectral and logarithmic spectrum [23].…”

Section: Speech Recognitionmentioning

confidence: 99%

Use Brain-Like Audio Features to Improve Speech Recognition Performance

Wang

Zhang

2022

Journal of Sensors

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Speech recognition plays an important role in the field of human-computer interaction through the use of acoustic sensors, but speech recognition is technically difficult, has complex overall logic, relies heavily on neural network algorithms, and has extremely high technical requirements. In speech recognition, feature extraction is the first step in speech recognition for recovering and extracting speech features. Existing methods, such as Meier spectral coefficients (MFCC) and spectrograms, lose a large amount of acoustic information and lack biological interpretability. Then, for example, existing speech self-supervised representation learning methods based on contrast prediction need to construct a large number of negative samples during training, and their learning effects depend on large batches of training, which requires a large amount of computational resources for the problem. Therefore, in this paper, we propose a new feature extraction method, called SHH (spike-H), that resembles the human brain and achieves higher speech recognition rates than previous methods. The features extracted using the proposed model are subsequently fed into the classification model. We propose a novel parallel CRNN model with an attention mechanism that considers both temporal and spatial features. Experimental results show that the proposed CRNN achieves an accuracy of 94.8% on the Aurora dataset. In addition, audio similarity experiments show that SHH can better distinguish audio features. In addition, the ablation experiments show that SHH is applicable to digital speech recognition.

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Section: Speech Recognitionmentioning

confidence: 99%