Gopala Krishna Anumanchipalli

Significance of early tagged contextual graphemes in grapheme based speech synthesis and recognition systems

In this paper we present our argument that context information could be used in early stages i.e., during the definition of mapping of the words into sequence of graphemes. We show that the early tagged contextual graphemes play a significant role in improving the performance of grapheme based speech synthesis and speech recognition systems.

Intent transfer in speech-to-speech machine translation

This paper presents an approach for transfer of speaker intent in speech-to-speech machine translation (S2SMT). Specifically, we describe techniques to retain the prominence patterns of the source language utterance through the translation pipeline and impose this information during speech synthesis in the target language. We first present an analysis of word focus across languages to motivate the problem of transfer. We then propose an approach for training an appropriate transfer function for intonation on a parallel speech corpus in the two languages within which the translation is carried out. We present our analysis and experiments on English↔Portuguese and English↔German language pairs and evaluate the proposed transformation techniques through objective measures.

Data-driven intonational phonology

Intonational Phonology deals with the systematic way in which speakers effectively use pitch to add appropriate emphasis to the underlying string of words in an utterance. Two widely discussed aspects of pitch are the pitch accents and boundary events. These provide an insight into the sentence type, speaker attitude, linguistic background, and other aspects of prosodic form. The main hurdle, however, is the difficulty in getting annotations of these attributes in “real” speech. Besides being language independent, these attributes are known to be subjective and prone to high inter-annotator disagreements. Our investigations aim to automatically derive phonological aspects of intonation from large speech databases. Recurring and salient patterns in the pitch contours, observed jointly with an underlying linguistic context are automatically detected. Our computational framework unifies complementary paradigms such as the physiological Fujisaki model, Autosegmental Metrical phonology, and elegant pitch styli...

A style capturing approach to F0 transformation in voice conversion

In this paper, we present a new approach to F0 transformation, that can capture aspects of speaking style. Instead of using the traditional 5ms frames as units in transformation, we propose a method that looks at longer phonological regions such as metrical feet. We automatically detect metrical feet in the source speech, and for each of source speakers feet, we find its phonological correspondence in target speech. We use a statistical phrase accent model to represent the F0 contour, where a 4-dimensional TILT representation is used for the F0 is parameterized over each feet region for the source and target speakers. This forms the parallel data that is the training data for our transformation. We transform the phrase component using simple z-score mapping. We use a joint density Gaussian mixture model to transform the accent contours. Our transformation method generates F0 contours that are significantly more correlated with the target speech than a baseline, frame-based method.