Evaluation of Formant-Like Features for ASR

This paper investigates possibilities to automatically find a low-dimensional, formant-related physical representation of the speech signal, which is suitable for automatic speech recognition (ASR). This aim is motivated by the fact that formants have been shown to be discriminant features for ASR. Combinations of automatically extracted formant-like features and `conventional', noise-robust, state-of-the-art features (such as MFCCs including spectral subtraction and cepstral mean subtraction) have previously been shown to be more robust in adverse conditions than state-of-the-art features alone. However, it is not clear how these automatically extracted formant-like features behave in comparison with true formants. The purpose of this paper is to investigate two methods to automatically extract formant-like features, and to compare these features to hand-labeled formant tracks as well as to standard MFCCs in terms of their performance on a vowel classification task

Reconstruction of Phonated Speech from Whispers Using Formant-Derived Plausible Pitch Modulation

Whispering is a natural, unphonated, secondary aspect of speech communications for most people. However, it is the primary mechanism of communications for some speakers who have impaired voice production mechanisms, such as partial laryngectomees, as well as for those prescribed voice rest, which often follows surgery or damage to the larynx. Unlike most people, who choose when to whisper and when not to, these speakers may have little choice but to rely on whispers for much of their daily vocal interaction. Even though most speakers will whisper at times, and some speakers can only whisper, the majority of today’s computational speech technology systems assume or require phonated speech. This article considers conversion of whispers into natural-sounding phonated speech as a noninvasive prosthetic aid for people with voice impairments who can only whisper. As a by-product, the technique is also useful for unimpaired speakers who choose to whisper. Speech reconstruction systems can be classified into those requiring training and those that do not. Among the latter, a recent parametric reconstruction framework is explored and then enhanced through a refined estimation of plausible pitch from weighted formant differences. The improved reconstruction framework, with proposed formant-derived artificial pitch modulation, is validated through subjective and objective comparison tests alongside state-of-the-art alternatives

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

Determination of Formant Features in Czech and Slovak for GMM Emotional Speech Classifier

The paper is aimed at determination of formant features (FF) which describe vocal tract characteristics. It comprises analysis of the first three formant positions together with their bandwidths and the formant tilts. Subsequently, the statistical evaluation and comparison of the FF was performed. This experiment was realized with the speech material in the form of sentences of male and female speakers expressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. The statistical distribution of the analyzed formant frequencies and formant tilts shows good differentiation between neutral and emotional styles for both voices. Contrary to it, the values of the formant 3-dB bandwidths have no correlation with the type of the speaking style or the type of the voice. These spectral parameters together with the values of the other speech characteristics were used in the feature vector for Gaussian mixture models (GMM) emotional speech style classifier that is currently developed. The overall mean classification error rate achieves about 18 %, and the best obtained error rate is 5 % for the sadness style of the female voice. These values are acceptable in this first stage of development of the GMM classifier that should be used for evaluation of the synthetic speech quality after applied voice conversion and emotional speech style transformation

Classification of Malaysian vowels using formant based features

Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software, especially using English as the language of choice. Despite of all these advances, machines cannot match the performance of their human counterparts in terms of accuracy and speed, especially in case of speaker independent speech recognition. In this paper, a new feature based on formant is presented and evaluated on Malaysian spoken vowels. These features were classified and used to identify vowels recorded from 80 Malaysian speakers. A back propagation neural network (BPNN) model was developed to classify the vowels. Six formant features were evaluated, which were the first three formant frequencies and the distances between each of them. Results, showed that overall vowel classification rate of these three formant combinations are comparatively the same but differs in terms of individual vowel classification

A computer model of auditory efferent suppression: Implications for the recognition of speech in noise

The neural mechanisms underlying the ability of human listeners to recognize speech in the presence of background noise are still imperfectly understood. However, there is mounting evidence that the medial olivocochlear system plays an important role, via efferents that exert a suppressive effect on the response of the basilar membrane. The current paper presents a computer modeling study that investigates the possible role of this activity on speech intelligibility in noise. A model of auditory efferent processing [ Ferry, R. T., and Meddis, R. (2007). J. Acoust. Soc. Am. 122, 3519?3526 ] is used to provide acoustic features for a statistical automatic speech recognition system, thus allowing the effects of efferent activity on speech intelligibility to be quantified. Performance of the ?basic? model (without efferent activity) on a connected digit recognition task is good when the speech is uncorrupted by noise but falls when noise is present. However, recognition performance is much improved when efferent activity is applied. Furthermore, optimal performance is obtained when the amount of efferent activity is proportional to the noise level. The results obtained are consistent with the suggestion that efferent suppression causes a ?release from adaptation? in the auditory-nerve response to noisy speech, which enhances its intelligibility

A summary of the 2012 JHU CLSP Workshop on Zero Resource Speech Technologies and Models of Early Language Acquisition

We summarize the accomplishments of a multi-disciplinary workshop exploring the computational and scientific issues surrounding zero resource (unsupervised) speech technologies and related models of early language acquisition. Centered around the tasks of phonetic and lexical discovery, we consider unified evaluation metrics, present two new approaches for improving speaker independence in the absence of supervision, and evaluate the application of Bayesian word segmentation algorithms to automatic subword unit tokenizations. Finally, we present two strategies for integrating zero resource techniques into supervised settings, demonstrating the potential of unsupervised methods to improve mainstream technologies.5 page(s