Gender discrimination and awareness are essentially practiced in social, education, workplace, economic sectors across the globe. A person manifests this attribute naturally gait, body gesture, facial, including speech. For that reason, automatic gender recognition (AGR) has become an interesting sub-topic speech systems can be found many technology applications. However, retrieving salient gen...

Query by Singing/Humming (QBSH) is a Music Information Retrieval (MIR) system with small audio excerpt as query. The rising availability of digital music stipulates effective music retrieval methods. Further, MIR systems support content based searching for music and requires no musical acquaintance. Current work on QBSH focuses mainly on melody features such as pitch, rhythm, note etc., size of...

Automatic Speech Recognition Systems of today are intensely deployed in real world application scenarios which are often characterized by suboptimal operating conditions. Thus their noise robustness has become a crucial parameter when assessing ASR in-field performance. The paper examines the noise robustness of traditional ASR feature sets as applied to a Voice Dialing Application built for Ma...

The mel-scaled frequency cepstral coefficients (MFCCs) derived from Fourier transform and filter bank analysis are perhaps the most widely used front-ends in state-of-the-art speech recognition systems. One of the major issues with the MFCCs is that they are very sensitive to additive noise. To improve the robustness of speech front-ends with respect to noise, we introduce, in this paper, a new...

Speaker Recognition (SR) is an economic method of biometrics because of availability of low cost and high power computers. An important question which must be answered for the SR system is how well the system resists the effects of determined mimics such as those based on physiological characteristics especially identical twins or triplets. In this paper, a new data fusion technique (viz., majo...

Time-Frequency Principal Component (TFPC) is a speech parameterization technique based on a principal component analysis applied to acoustic feature parameters augmented by their time context. In this paper, we investigate on the performance of TFPC in the framework of automatic language recognition. In our experiments, identification rate is improved compared to the use of the conventional cep...