Collapsed speech segment detection and suppression for WaveNet vocoder

In this paper, we propose a technique to alleviate the quality degradation caused by collapsed speech segments sometimes generated by the WaveNet vocoder. The effectiveness of the WaveNet vocoder for generating natural speech from acoustic features has been proved in recent works. However, it sometimes generates very noisy speech with collapsed speech segments when only a limited amount of training data is available or significant acoustic mismatches exist between the training and testing data. Such a limitation on the corpus and limited ability of the model can easily occur in some speech generation applications, such as voice conversion and speech enhancement. To address this problem, we propose a technique to automatically detect collapsed speech segments. Moreover, to refine the detected segments, we also propose a waveform generation technique for WaveNet using a linear predictive coding constraint. Verification and subjective tests are conducted to investigate the effectiveness of the proposed techniques. The verification results indicate that the detection technique can detect most collapsed segments. The subjective evaluations of voice conversion demonstrate that the generation technique significantly improves the speech quality while maintaining the same speaker similarity.Comment: 5 pages, 6 figures. Proc. Interspeech, 201

Linguistically-constrained formant-based i-vectors for automatic speaker recognition

This is the author’s version of a work that was accepted for publication in Speech Communication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Speech Communication, VOL 76 (2016) DOI 10.1016/j.specom.2015.11.002This paper presents a large-scale study of the discriminative abilities of formant frequencies for automatic speaker recognition. Exploiting both the static and dynamic information in formant frequencies, we present linguistically-constrained formant-based i-vector systems providing well calibrated likelihood ratios per comparison of the occurrences of the same isolated linguistic units in two given utterances. As a first result, the reported analysis on the discriminative and calibration properties of the different linguistic units provide useful insights, for instance, to forensic phonetic practitioners. Furthermore, it is shown that the set of units which are more discriminative for every speaker vary from speaker to speaker. Secondly, linguistically-constrained systems are combined at score-level through average and logistic regression speaker-independent fusion rules exploiting the different speaker-distinguishing information spread among the different linguistic units. Testing on the English-only trials of the core condition of the NIST 2006 SRE (24,000 voice comparisons of 5 minutes telephone conversations from 517 speakers -219 male and 298 female-), we report equal error rates of 9.57 and 12.89% for male and female speakers respectively, using only formant frequencies as speaker discriminative information. Additionally, when the formant-based system is fused with a cepstral i-vector system, we obtain relative improvements of ∼6% in EER (from 6.54 to 6.13%) and ∼15% in minDCF (from 0.0327 to 0.0279), compared to the cepstral system alone.This work has been supported by the Spanish Ministry of Economy and Competitiveness (project CMC-V2: Caracterizacion, Modelado y Compensacion de Variabilidad en la Señal de Voz, TEC2012-37585-C02-01). Also, the authors would like to thank SRI for providing the Decipher phonetic transcriptions of the NIST 2004, 2005 and 2006 SREs that have allowed to carry out this work

Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure