We present the development of robust speech recognition techniques for voice activated dialing (VAD) for a cellular phone in a car. Noise reduction techniques are implemented before modeling the speech parameters in the homomorphic domain. The FFT derived cepstral coefficients are liftered and then Mel-scale warped to generate the feature vector. A radial basis function neural network is used as the final classifier and variation in its structure is studied to evaluate its real-time performance. Results on speaker-dependent and speaker-independent recognition across -5 dB to 25 dB SNR range are presented and a performance evaluation is carried out for different front-end techniques. The system is evaluated using the NOISEX-92 database.
展开▼
机译：我们介绍了用于汽车中蜂窝电话的语音激活拨号（VAD）的健壮语音识别技术的开发。在对同态域中的语音参数进行建模之前，应先实施降噪技术。提升FFT得出的倒谱系数，然后对梅尔标度进行扭曲以生成特征向量。使用径向基函数神经网络作为最终分类器，并研究其结构的变化以评估其实时性能。给出了-5 dB至25 dB SNR范围内与说话者相关和与说话者无关的识别结果，并对不同的前端技术进行了性能评估。使用NOISEX-92数据库评估系统。
展开▼
