Speech-Signal-Based Frequency Warping

Abstract

The speech signal is used for transmission of linguistic information. High energy portions of the speech spectrum have higher signal-to-noise ratios than the low energy portions. As a result, these regions are more robust to noise. Since the speech signal is known to be very robust to noise, it is expected that the high energy regions of the speech spectrum carry the majority of the linguistic information. This letter tries to derive a frequency warping function directly from the speech signal by sampling the frequency axis nonuniformly with the high energy regions sampled more densely than the low energy regions. To achieve this, an ensemble average short-time power spectrum is computed from a large speech corpus. The speech-signal-based frequency warping is obtained by considering equal area portions of the log spectrum. The proposed frequency warping is shown to be similar to the frequency scales obtained through psycho-acoustic experiments, namely the mel and bark scales. The warping is then used in filterbank design for automatic speech recognition experiments. The results of these experiments show that cepstral features based on the proposed warping achieve performance under clean conditions comparable to that of mel-frequency cepstral coefficients, while outperforming them under noisy conditions.