Modified Mel Filter Bank to Compute MFCC of Subsampled Speech
Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in most speech and speaker recognition applications. In this work, we propose a modified Mel filter bank to extract MFCCs from subsampled speech. We also propose a stronger metric which effectively captures the correlation between MFCCs of original speech and MFCC of resampled speech. It is found that the proposed method of filter bank construction performs distinguishably well and gives recognition performance on resampled speech close to recognition accuracies on original speech.
Code (0)
등록된 구현이 없습니다.
Tasks
Speaker RecognitionSimilar Papers 제목 키워드 기반
Choice of Mel Filter Bank in Computing MFCC of a Resampled Speech
Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in most speech and speaker recognition applications. In this paper, we study the effect of resampling a speech signal on these speec…
Speaker RecognitionOptimization of data-driven filterbank for automatic speaker verification
Most of the speech processing applications use triangular filters spaced in mel-scale for feature extraction. In this paper, we propose a new data-driven filter design method which optimizes filter parameters from a give…
Speaker VerificationSpeech waveform synthesis from MFCC sequences with generative adversarial networks
This paper proposes a method for generating speech from filterbank mel frequency cepstral coefficients (MFCC), which are widely used in speech applications, such as ASR, but are generally considered unusable for speech s…
Generative Adversarial NetworkSpeech SynthesisLearnable MFCCs for Speaker Verification
We propose a learnable mel-frequency cepstral coefficient (MFCC) frontend architecture for deep neural network (DNN) based automatic speaker verification. Our architecture retains the simplicity and interpretability of M…
Speaker VerificationFrequency-centroid features for word recognition of non-native English speakers
The objective of this work is to investigate complementary features which can aid the quintessential Mel frequency cepstral coefficients (MFCCs) in the task of closed, limited set word recognition for non-native English …