We propose an effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary nonlinear boundary in a feature space and is known to yield high classification performance. In the present work, we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a features fusion scheme based on a combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover, the performance is substantially improved when the proposed features fusion technique is applied.

This paper describes a new method to detect speech segments online with identifying gender attributes for efficient dual gender-dependent speech recognition and broadcast news captioning. The proposed online speech detection performs dual-gender phoneme recognition and detects a start-point and an end-point based on the ratio between the cumulative phoneme likelihood and the cumulative non-speech likelihood with a very small delay from the audio input. Obtaining the speech segments, the phoneme recognizer also identifies gender attributes with high discrimination in order to guide the subsequent dual-gender continuous speech recognizer efficiently. As soon as the start-point is detected, the continuous speech recognizer with paralleled gender-dependent acoustic models starts a search and allows search transitions between male and female in a speech segment based on the gender attributes. Speech recognition experiments on conversational commentaries and field reporting from Japanese broadcast news showed that the proposed speech detection method was effective in reducing the false rejection rate from 4.6% to 0.53% and also recognition errors in comparison with a conventional method using adaptive energy thresholds. It was also effective in identifying the gender attributes, whose correct rate was 99.7% of words. With the new speech detection and the gender identification, the proposed dual-gender speech recognition significantly reduced the word error rate by 11.2% relative to a conventional gender-independent system, while keeping the computational cost feasible for real-time operation.