This paper proposes an algorithm for speech parameter generation from HMMs which include the dynamic features. The performance of speech recognition based on HMMs has been improved by introducing the dynamic features of speech. Thus we surmise that, if there is a method for speech parameter generation from HMMs which include the dynamic features, it will be useful for speech synthesis by rule. It is shown that the parameter generation from HMMs using the dynamic features results in searching for the optimum state sequence and solving a set of linear equations for each possible state sequence. We derive a fast algorithm for the solution by the analogy of the RLS algorithm for adaptive filtering. We also show the effect of incorporating the dynamic features by an example of speech parameter generation.

Speech parameter generation from HMM using dynamic features

This paper presents a new text-to-speech synthesis system based on HMM which includes dynamic features, i.e., delta and delta-delta parameters of speech. The system uses triphone HMMs as the synthesis units. The triphone HMMs share less than 2,000 clustered states, each of which is modelled by a single Gaussian distribution. For a given text to be synthesized, a sentence HMM is constructed by concatenating the triphone HMMs. Speech parameters are generated from the sentence HMM in such a way that the output probability is maximized. The speech signal is synthesized directly from the obtained parameters using the mel log spectral approximation (MLSA) filter. Without dynamic features, the discontinuity of the generated speech spectra causes glitches in the synthesized speech. On the other hand, with dynamic features, the synthesized speech becomes quite smooth and natural even if the number of clustered states is small.

Speech synthesis using HMMs with dynamic features

Recognition of handwritten word: first and second order hidden Markov model based approach

This paper proposes an algorithm for speech parameter generation from continuous mixture HMMs which include dynamic features, i.e., delta and delta-delta parameters of speech. We show that the parameter generation from HMMs using the dynamic features results in searching for the optimal state sequence and solving a set of linear equations for each possible state sequence. To solve the problem, we derive a fast algorithm on the analogy of the RLS algorithm for adaptive ltering. We show that the generated speech parameter vectors re ect not only the means of static and dynamic feature vectors but also the covariances of those. An example presenting e ectiveness of the proposed algorithm in speech synthesis is given.

An algorithm for speech parameter generation from continuous mixture HMMs with dynamic features

https://hal.archives-ouvertes.fr/hal-00416724/document

SFC: a trainable prosodic model

A novel technique is introduced for characterizing prosodic structure and is used for speech synthesis. The mechanism consists of modeling a set of observations as a probabilistic function of a hidden Markov chain. It uses mixtures of Gaussian continuous probability density functions to represent the essential, perceptually relevant structure of intonation by observing movements of fundamental frequency in monosyllabic words of varying phonetic structure. High-quality speech synthesis, using multipulse excitation, is used to demonstrate the power of the HMM in preserving the naturalness of the intonational meaning, conveyed by the variation of fundamental frequency and duration. The fundamental frequency contours are synthesized using a random number generator from the models, and are imposed on a synthesized prototype word which had the intonation of a low fall. The resulting monosyllabic words with imposed synthesized fundamental frequency contours show a high level of naturalness and are found to be perceptually indistinguishable from the original recordings with the same intonation. The results clearly show the high potential of hidden Markov models as a mechanism for the representation of prosodic structure by naturally capturing its essentials.

F. Fallside

Papers

Synthesis of natural sounding pitch contours in isolated utterances using hidden Markov models