Digital processing of speech signals

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Intelligent Automated Assistant

This paper gives a general overview of techniques in statistical parametric speech synthesis. One of the instances of these techniques, called HMM-based generation synthesis (or simply HMM-based synthesis), has recently been shown to be very effective in generating acceptable speech synthesis. This paper also contrasts these techniques with the more conventional unit selection technology that has dominated speech synthesis over the last ten years. Advantages and disadvantages of statistical parametric synthesis are highlighted as well as identifying where we expect the key developments to appear in the immediate future.

https://www.sp.nitech.ac.jp/~bonanza/Paper/EMIME/zen_specom.pdf

Statistical Parametric Speech Synthesis

In this paper, we describe a novel spectral conversion method for voice conversion (VC). A Gaussian mixture model (GMM) of the joint probability density of source and target features is employed for performing spectral conversion between speakers. The conventional method converts spectral parameters frame by frame based on the minimum mean square error. Although it is reasonably effective, the deterioration of speech quality is caused by some problems: 1) appropriate spectral movements are not always caused by the frame-based conversion process, and 2) the converted spectra are excessively smoothed by statistical modeling. In order to address those problems, we propose a conversion method based on the maximum-likelihood estimation of a spectral parameter trajectory. Not only static but also dynamic feature statistics are used for realizing the appropriate converted spectrum sequence. Moreover, the oversmoothing effect is alleviated by considering a global variance feature of the converted spectra. Experimental results indicate that the performance of VC can be dramatically improved by the proposed method in view of both speech quality and conversion accuracy for speaker individuality.

/pdf/voice-conversion-based-on-maximum-likelihood-estimation-of-ysizn2pm2o.pdf

Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory

INTERSPEECH2008: 9th Annual Conference of the International Speech Communication Association, September 22-26, 2008, Brisbane, Australia.

Low-delay voice conversion based on maximum likelihood estimation of spectral parameter trajectory.

A new voice conversion algorithm that modifies a source speaker's speech to sound as if produced by a target speaker is presented. It is applied to a residual-excited LPC text-to-speech diphone synthesizer. Spectral parameters are mapped using a locally linear transformation based on Gaussian mixture models whose parameters are trained by joint density estimation. The LPC residuals are adjusted to match the target speakers average pitch. To study effects of the amount of training on performance, data sets of varying sizes are created by automatically selecting subsets of all available diphones by a vector quantization method. In an objective evaluation, the proposed method is found to perform more reliably for small training sets than a previous approach. In perceptual tests, it was shown that nearly optimal spectral conversion performance was achieved, even with a small amount of training data. However, speech quality improved with increases in the training set size.

Spectral voice conversion for text-to-speech synthesis

A method of singing voice synthesis uses commercially-available MIDI-based music composition software as a user interface (13). The user specifies a musical score and lyrics; as well as other music control parameters. The control information is stored in a MIDI file (11). Based on the input to the MIDI file (11) the system selects synthesis model parameters from an inventory (15) of linguistic voice data units. The units are selected and concatenated in a linguistic processor (17). The units are smoothed in the processing and are modified according to the music control parameters in musical processor (19) to modify the pitch, duration, and spectral characteristics of the concatenated voice units as specified by the musical score. The output waveform is synthesized using a sinusoidal model 20.

Singing voice synthesis

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.

Universal speech tools: the CSLU toolkit.

The purpose of a voice conversion (VC) system is to change the perceived speaker identity of a speech signal. We propose an algorithm based on converting the LPC spectrum and predicting the residual as a function of the target envelope parameters. We conduct listening tests based on speaker discrimination of same/difference pairs to measure the accuracy by which the converted voices match the desired target voices. To establish the level of human performance as a baseline, we first measure the ability of listeners to discriminate between original speech utterances under three conditions: normal, fundamental frequency and duration normalized, and LPC coded. Additionally, the spectral parameter conversion function is tested in isolation by listening to source, target, and converted speakers as LPC coded speech. The results show that the speaker identity of speech whose LPC spectrum has been converted can be recognized as the target speaker with the same level of performance as discriminating between LPC coded speech. However, the level of discrimination of converted utterances produced by the full VC system is significantly below that of speaker discrimination of natural speech.

Design and evaluation of a voice conversion algorithm based on spectral envelope mapping and residual prediction

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 di erences 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 di erences 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.

Michael W. Macon

Papers

Spectral voice conversion for text-to-speech synthesis

Singing voice synthesis

Universal speech tools: the CSLU toolkit.

Design and evaluation of a voice conversion algorithm based on spectral envelope mapping and residual prediction

A perceptual evaluation of distance measures for concatenative speech synthesis.