In this study, a discriminative weight training is applied to a support vector machine (SVM) based speech/music classification for a 3GPP2 selectable mode vocoder (SMV). In the proposed approach, the speech/music decision rule is derived by the SVM by incorporating optimally weighted features derived from the SMV based on a minimum classification error (MCE) method. This method differs from tha...

The paper analyzes the prosody features, which includes the intonation, speaking rate, intensity, based on classified emotional speech. As an important feature of voice quality, voice source are also deduced for analysis. With the analysis results above, the paper creates both a CART model and a weight decay neural network model to find acoustic importance towards the emotional speech classific...

Concept classifiers have been used in speech to speech translation systems. Their effectiveness, however, depends on the size of the domain that they cover. The main bottleneck in expanding the classifier domain is the degradation in accuracy as the number of classes increase. Here we introduce a hierarchical classification process that aims to scale up the domain without compromising the accur...

We propose an algorithm to classify speech degradations at network endpoints and to estimate the speech quality based on the degradation classification decision. Perceptual features from degraded speech signals are used to form statistical reference models of different degradation classes. Consistency measures, calculated between degraded speech signals and the reference models, are used to tra...

Speaker’s intentions can be represented into domain actions (domainindependent speech acts and domain-dependent concept sequences). Therefore, domain action classification is very useful to a dialogue system that should catch user’s intention in order to generate correct reaction. In this paper, we propose a neural network model to determine speech acts and concept sequences at the same time. T...

With ever-increasing number of car-mounted electric devices and their complexity, audio classification is increasingly important for the automotive industry as a fundamental tool for human-device interactions. Existing approaches for audio classification, however, fall short as the unique and dynamic audio characteristics of in-vehicle environments are not appropriately taken into account. In t...

In a speech recognition system a Voice Activity Detector (VAD) is a crucial component for not only maintaining accuracy but also for reducing computational consumption. Front-end approaches which drop non-speech frames typically attempt to detect speech frames by utilizing speech/non-speech classification information such as the zero crossing rate or statistical models. These approaches discard...

The paper analyzes the prosody features, which includes the intonation, speaking rate, intensity, based on classified emotional speech. As an important feature of voice quality, voice source are also deduced for analysis. With the analysis results above, the paper creates both a CART model and a weight decay neural network model to find acoustic importance towards the emotional speech classific...