Estimating Speaking Rate by Means of Rhythmicity Parameters

In this paper we present a speech rate estimator based on so-called rhythmicity features derived from a modified version of the short-time energy envelope. To evaluate the new method, it is compared to a traditional speech rate estimator on the basis of semi-automatic segmentation. Speech material from the Alcohol Language Corpus (ALC) covering intoxicated and sober speech of different speech styles provides a statistically sound foundation to test upon. The proposed measure clearly correlates with the semi-automatically determined speech rate and seems to be robust across speech styles and speaker states

Comparación de dos métodos basados en la intensidad para el cálculo automático de la velocidad de habla

Automatic computation of speech rate is a necessary task in a wide range of applications that require this prosodic feature, in which a manual transcription and time alignments are not available. Several tools have been developed to this end, but not enough research has been conducted yet to see to what extent they are scalable to other languages. In the present work, we take two off-the- shelf tools designed for automatic speech rate computation and already tested for Dutch and English (v1, which relies on intensity peaks preceded by an intensity dip to find syllable nuclei and v3, which relies on intensity peaks surrounded by dips) and we apply them to read and spontaneous Spanish speech. Then, we test which of them offers the best performance. The results obtained with precision and normalized mean squared error metrics showed that v3 performs better than v1. However, recall measurement shows a better performance of v1, which suggests that a more fine-grained analysis on sensitivity and specificity is needed to select the best option depending on the application we are dealing with.El cálculo automático de la velocidad de habla es una tarea fonética útil y que además se hace indispensable cuando no hay disponible una transcripción manual a partir de la cual determinar una tasa de habla manual. Se han desarrollado varias herramientas para este fin, pero todavía no se ha llevado a cabo suficiente investigación para ver hasta qué punto las herramientas son aplicables a lenguas distintas para las que fueron diseñadas. En este artículo probamos dos herramientas para el cálculo automático de la velocidad de habla ya evaluadas para el neerlandés y el inglés (v1, que se basa en la determinación de picos de intensidad precedidos de un valle para encontrar núcleos de sílaba, y v3, que se basa en picos de intensidad rodeados de valles) y las aplicamos a un corpus de habla leída y espontánea del español para analizar cuál ofrece mejores resultados en español. Los resultados de precisión y del error cuadrático mediano normalizado obtenidos muestran que v3 funciona mejor que v1. No obstante, el recall muestra mejor rendimiento para la v1, lo que nos indica que se necesita un análisis detallado de la sensibilidad y la especificidad para seleccionar la mejor opción en función de los objetivos del análisis posterior que se quiera hacer

Unsupervised spoken keyword spotting and learning of acoustically meaningful units

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.Cataloged from PDF version of thesis.Includes bibliographical references (p. 103-106).The problem of keyword spotting in audio data has been explored for many years. Typically researchers use supervised methods to train statistical models to detect keyword instances. However, such supervised methods require large quantities of annotated data that is unlikely to be available for the majority of languages in the world. This thesis addresses this lack-of-annotation problem and presents two completely unsupervised spoken keyword spotting systems that do not require any transcribed data. In the first system, a Gaussian Mixture Model is trained to label speech frames with a Gaussian posteriorgram, without any transcription information. Given several spoken samples of a keyword, a segmental dynamic time warping is used to compare the Gaussian posteriorgrams between keyword samples and test utterances. The keyword detection result is then obtained by ranking the distortion scores of all the test utterances. In the second system, to avoid the need for spoken samples, a Joint-Multigram model is used to build a mapping from the keyword text samples to the Gaussian component indices. A keyword instance in the test data can be detected by calculating the similarity score of the Gaussian component index sequences between keyword samples and test utterances. The proposed two systems are evaluated on the TIMIT and MIT Lecture corpus. The result demonstrates the viability and effectiveness of the two systems. Furthermore, encouraged by the success of using unsupervised methods to perform keyword spotting, we present some preliminary investigation on the unsupervised detection of acoustically meaningful units in speech.by Yaodong Zhang.S.M

Untersuchungen der rhythmischen Struktur von Sprache unter Alkoholeinfluss

This thesis is concerned with the rhythmical structure of speech under the influence of alcohol. All analyses presented are based on the Alcohol Language Corpus, which is a collection of speech uttered by 77 female and 85 male sober and intoxicated speakers. Experimental research was carried out to find robust, automatically extractable features of the speech signal that indicate speaker intoxication. These features included rhythm measures, which reflect the durational variability of vocalic and consonantal elements and are normally used to classify languages into different rhythm classes. The durational variability was found to be greater in the speech of intoxicated individuals than in the speech of sober individuals, which suggests, that speech of intoxicated speakers is more irregular than speech of sober speakers. Another set of features describes the dynamics of the short-time energy function of speech. Therefore different measures are derived from a sequence of energy minima and maxima. The results also reveal a greater irregularity in the speech of intoxicated individuals. A separate investigation about speaking rate included two different measures. One is based on the phonetic segmentation and is an estimate of the number of syllables per second. The other is the mean duration of the time intervals between successive maxima of the short-time energy function of speech. Both measures denote a decreased speaking rate in the speech of intoxicated speakers compared to speech uttered in sober condition. The results of a perception experiment show that a decrease in speaking rate also is an indicator for intoxication in the perception of speech. The last experiment investigates rhythmical features based on the fundamental frequency and energy contours of speech signals. Contours are compared directly with different distance measures (root mean square error, statistical correlation and the Euclidean distance in the spectral space of the contours). They are also compared by parameterization of the contours using Discrete Cosine Transform and the first and second moments of the lower DCT spectrum. A Principal Components Analysis on the contour data was also carried out to find fundamental contour forms regarding the speech of intoxicated and sober individuals. Concerning the distance measures, contours of speech signals uttered by intoxicated speakers differ significantly from contours of speech signals uttered in sober condition. Parameterization of the contours showed that fundamental frequency contours of speech signals uttered by intoxicated speakers consist of faster movements and energy contours of speech signals uttered by intoxicated speakers of slower movements than the respective contours of speech signals uttered in sober condition. Principal Components Analysis did not find any interpretable fundamental contour forms that could help distinguishing contours of speech signals of intoxicated speakers from those of speech uttered in sober condition. All analyses prove that the effects of alcoholic intoxication on different features of speech cannot be generalized but are to a great extent speaker-dependent