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We study user-friendly voice interface to consumer electronics and propose a voice activation system that can make speech recognition activated only when voice sounds from legitimate users are detected. The proposed system enables efficient operation of speech recognition in a continuous listening environment without any touch and/or key input.
user, it can be utilized as a trigger. In this paper, we propose a voice trigger system using a keyword-dependent speaker recognition technique. The voice trigger must be able to perform keyword recognition, as well as speaker recognition, without using computationally demanding speech recognizers to properly trigger a
This paper proposes a novel system for robust keyword detection in continuous speech. Our decoder is composed of a bidirectional Long Short-Term Memory recurrent neural network using a Connectionist Temporal Classification (CTC) output layer, and a Dynamic Bayesian Network (DBN). The CTC network exploits bidirectional
keyword, and terminated in similar fashion with de-activation keyword. Speaker recognition is performed on the activation keyword to allow personalization of the voice commands available to the particular user, who in this scenario is a member of the household. A separate setting is also devised to enable guest user to have
In this paper, the effect of keyword choice including and excluding plosive sounds on isolated speaker recognition system is investigated. In order to perform this study, a Turkish word database has been created consisting of 48 words including plosives and 7 words without plosives. Records are acquired at a sampling
a word-dependent system using the Arabic isolated word /ns10 as10 cs10 as10 ms10//[unk]/ a single keyword for the test utterance. This choice has been made because the word /ns10 as10 cs10 as10 ms10//[unk]/ is mostly used by the Arabic speakers. Speech features are extracted using MFCC. The HTK is used to implement the
This paper describes ICSI's 2005 speaker recognition system, which was one of the top performing systems in the NIST 2005 speaker recognition evaluation. The system is a combination of four sub-systems: 1) a keyword conditional HMM system, 2) an SVM-based lattice phone n-gram system, 3) a sequential nonparametric
This paper presents a new method for Vietnamese text-dependent speaker recognition. The system is modeled for each speaker using mixture model Gaussian GMM (Gaussian Mixture Model). The phonemes in the keywords are represented by hidden Markov models HMM. The prior and posterior probabilities for keywords and speakers
This study analyzes the effect of stress in human and automatic stressed speech processing tasks for speech collected from non-professional speakers. The database of 33 keywords is collected under five stress conditions, namely, neutral, angry, happy, sad and Lombard from fifteen speakers. The first study is to
and converts it into routing keywords. Accent identification is the most important factor for improving the performance of natural language call-routing systems because accents vary widely, even within the same country or community. This variation occurs when non-native speakers start to learn a second language; the
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