Current speech synthesis methods based on the concatenation of waveform units can produce highly intelligible speech capturing the identity of a particular speaker. However, the quality of concatenated speech often suffers from discontinuities between the acoustic units, due to contextual differences and variations in speaking style across the database. In this paper, we present methods to spectrally modify speech units in a concatenative synthesizer to correspond more closely to the acoustic transitions observed in natural speech. First, a technique called "unit fusion" is proposed to reduce spectral mismatch between units. In addition to concatenation units, a second, independent tier of units is selected that defines the desired spectral dynamics at concatenation points. Both unit tiers are "fused" to obtain natural transitions throughout the synthesized utterance. The unit fusion method is further extended to control the perceived degree of articulation of concatenated units. A signal processing technique based on sinusoidal modeling is also presented that enables high-quality resynthesis of units with a modified spectral shape.

Concatenative synthesis can produce high-quality speech but is limited to the allophonic variations and voice types that were captured in the database. It would be desirable to modify speech units to remove formant discontinuities and to create new speaking styles, such as hypo- or hyper-articulated speech. Unfortunately, manipulating the spectral structure often leads to degraded speech quality. We investigate two speech modification strategies, one based on inverse filtering and the other on sinusoidal modeling, and we explain their merits and shortcomings for changing the spectral envelope in speech. We then propose a method which uses sinusoidal modeling and represents the complex sinusoidal amplitudes by an all-pole model. The all-pole model approximates the sinusoidal spectrum well, both in the amplitude and in the phase domain. We use the sinusoidal+all-pole model to control the spectral envelope in recorded speech. High-quality modified speech is generated from the model using sinusoidal synthesis. A perceptual test was conducted, which shows that the model was effective at changing vowel identities and was preferable over residual excited LPC.

Text-to-speech synthesis research has moved away from building general purpose systems based on an understanding of human language and speech production towards building systems based on statistical algorithms applied to large text and speech corpora, and, recently, towards building such systems for specific domains. Despite substantial progress, the overall quality of even the best systems is often still inadequate for broad user acceptance in applications that cannot also be handled with simple phrase splicing. This tutorial paper analyzes which problems must be addressed to achieve the goal of generating naturalsounding speech in limited domains in a cost-effective way, and the roles of data and rules as we work towards solutions. 1.

An important problem in concatenative synthesis is the occurence of spectral discontinuities or "concatenation mismatch" between sonorant speech units. In this paper, we present an approach to reduce concatenation mismatch by combining spectral information from two sequences of speech units selected in parallel. Concatenation units, on one hand, define initial spectral trajectories for a target utterance. Fusion units, on the other hand, define the desired transitions between concatenated units. The two unit sequences are "fused" by imposing dynamic constraints defined by the fusion units on the spectral trajectories of the concatenation units. To regenerate the modified speech units, we use a synthesis algorithm based on sinusoidal + all-pole analysis of speech, which overcomes the limitations of residual-excited LPC. Results from a perceptual test show that our method is highly successful at removing concatenation artifacts in speech generated from an inventory of diphones.

A set of freely available, universal speech tools is needed to accelerate progress in the speech technology. The CSLU Toolkit represents an effort to make the core technology and fundamental infrastructure accessible, affordable and easy to use. The CSLU Toolkit has been under development for five years. This paper describes recent improvements, additions and uses of the CSLU Toolkit. 1. INTRODUCTION Since 1993, the Center for Spoken Language Understanding (CSLU) has focused on incorporating state-of-the-art spokenlanguage technology into a portable, comprehensive and easyto -use software environment. The result of these efforts is the CSLU Toolkit. The toolkit integrates learning materials, authoring tools and core technologies such as speech recognition, text-to-speech synthesis, facial animation and speech reading. The toolkit is designed to support basic research, development and education activities related to spoken language systems and human-computer interfaces. What are our ...

In concatenative synthesis, new utterances are created by concatenating segments (units) of recorded speech. When the segments are extracted from a large speech corpus, a key issue is to select segments that will sound natural in a given phonetic context. Distance measures are often used for this task. However, little is known about the perceptual relevance of these measures. More insight into the relationship between computed distances and perceptual differences is needed to develop accurate unit selection algorithms, and to improve the quality of the resulting computer speech. In this paper, we develop a perceptual test to measure subtle phonetic differences between speech units. We use the perceptual data to evaluate several popular distance measures. The results show that distance measures that use frequency warping perform better than those that do not, and minimal extra advantage is gained by using weighted distances or delta features. 1 INTRODUCTION To produce high quality con...