6,492 research outputs found
Glottal Source Cepstrum Coefficients Applied to NIST SRE 2010
Through the present paper, a novel feature set for speaker recognition based on glottal estimate information is presented. An iterative algorithm is used to derive the vocal tract and glottal source estimations from speech signal. In order to test the importance of glottal source information in speaker characterization, the novel feature set has been tested in the 2010 NIST Speaker Recognition Evaluation (NIST SRE10). The proposed system uses glottal estimate parameter templates and classical cepstral information to build a model for each speaker involved in the recognition process. ALIZE [1] open-source software has been used to create the GMM models for both background and target speakers. Compared to using mel-frequency cepstrum coefficients (MFCC), the misclassification rate for the NIST SRE 2010 reduced from 29.43% to 27.15% when glottal source features are use
English Broadcast News Speech Recognition by Humans and Machines
With recent advances in deep learning, considerable attention has been given
to achieving automatic speech recognition performance close to human
performance on tasks like conversational telephone speech (CTS) recognition. In
this paper we evaluate the usefulness of these proposed techniques on broadcast
news (BN), a similar challenging task. We also perform a set of recognition
measurements to understand how close the achieved automatic speech recognition
results are to human performance on this task. On two publicly available BN
test sets, DEV04F and RT04, our speech recognition system using LSTM and
residual network based acoustic models with a combination of n-gram and neural
network language models performs at 6.5% and 5.9% word error rate. By achieving
new performance milestones on these test sets, our experiments show that
techniques developed on other related tasks, like CTS, can be transferred to
achieve similar performance. In contrast, the best measured human recognition
performance on these test sets is much lower, at 3.6% and 2.8% respectively,
indicating that there is still room for new techniques and improvements in this
space, to reach human performance levels.Comment: \copyright 2019 IEEE. Personal use of this material is permitted.
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Development of a speech recognition system for Spanish broadcast news
This paper reports on the development process of a speech recognition system for Spanish broadcast news within the MESH FP6 project. The system uses the SONIC recognizer developed at the Center for Spoken Language Research (CSLR), University of Colorado. Acoustic and language models were trained using Hub4 broadcast news data. Experiments and evaluation results are reported
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