We present a method for estimating the vocal-tract area function from specified formant frequencies. The method extends the work of Story (J.A.S.A., 119, 715-718, 1996) based on a sensitivity function representing the change in the formant frequency due to a small perturbation of the cross-sectional area of the vocal tract. Our method estimates the vocal-tract shape through an iterative procedu...

The perceptual grouping of a four-tone cycle was studied as a function of differences in fundamental frequencies and the frequencies of spectral peaks. Each tone had a single formant and at least 13 harmonics. In Experiment 1 the formant was created by filtering a flat spectrum and in Experiment 2 by adding harmonics. Fundamental frequency was found to be capable of controlling grouping even wh...

A method has been developed for three-dimensional reconstruction of the vocal tract shape and for the calculation of area function from magnetic resonance imaging (MRI). MR images were acquired and analyzed for 6 German long vowels uttered by one subject. The resulting vocal tract area functions are the basis for the calculation of formant frequencies. Conformity with acoustically measured form...

Pitch and formant frequencies are important features in speech which are used to identify the emotional state of a person. The Pitch and Formants are first extracted from the speech signal and then their analysis is carried out to recognize 3 different emotional states of the person. The emotions considered are Neutral, Happy and Sad. The TTS-GU database has been used for the analysis. The Ceps...

Although laughter is an important aspect of nonverbal vocalization, its acoustic properties are still not fully understood. Extreme articulation during laughter production, such as wide jaw opening, suggests that laughter can have very high first formant (F(1)) frequencies. We measured fundamental frequency and formant frequencies of the vowels produced in the vocalic segments of laughter. Voca...

This paper describes a unifying framework for both formant tracking and speech synthesis using Hidden Markov Models (HMM). The feature vector in the HMM is composed by the first three formant frequencies, their bandwidths and their delta with time. Speech is synthesized by generating the most likely sequence of feature vectors from a HMM, trained with a set of sentences from a given speaker. Hi...

We studied tongue positions of vowels in oral and nasal contexts. In the previous study [Arai, J. Acoust. Soc. Am., 115, p.2541 (2004)], formant frequencies were measured and bidirectional formant shifts in F1 frequency were observed: increasing F1 for high vowels and decreasing F1 for low vowels. Then, we tried to answer to the next question, that is, whether or not speakers and/or listeners c...

The measurement of formant frequencies of vowels is among the most common measurements in speech studies, but measurements are known to be biased by the particular fundamental frequency (F0) exciting the formants. Approaches to reducing the errors were assessed in two experiments. In the first, synthetic vowels were constructed with five different first formant (F1) values and nine different F0...

In this paper, a probabilistic model is introduced to obtain feature-based likelihood ratios from linguistically-constrained formant-based i-vectors in a NIST SRE task. Linguisticallyconstrained formant-based i-vectors summarize both the static and dynamic information of formant frequencies in the occurrences of a given linguistic unit in a speech recording. In this work, a two-covariance model...

This work proposes a novel method of predicting formant frequencies from a stream of mel-frequency cepstral coefficients (MFCC) feature vectors. Prediction is based on modelling the joint density of MFCC vectors and formant vectors using a Gaussian mixture model (GMM). Using this GMM and an input MFCC vector, two maximum a posteriori (MAP) prediction methods are developed. The first method pred...