Spoken interaction tasks are typically approached using a formal grammar as language model. While ensuring good system performance, this imposes a rigid framework on users, by implicitly forcing them to conform to a pre-defined interaction structure. This paper introduces the concept of data-driven semantic inference, which in principle allows for any word constructs in command/query formulation. Each unconstrained word string is automatically mapped onto the intended action through a semantic classification against the set of supported actions. As a result, it is no longer necessary for users to memorize the exact syntax of every command. The underlying (latent semantic analysis) framework relies on co-occurrences between words and commands, as observed in a training corpus. A suitable extension can also handle commands that are ambiguous at the word level. The behavior of semantic inference is characterized using a desktop user interface control task involving 113 different actions. Under realistic usage conditions, this approach exhibits a 2 to 5% classification error rate. Various training scenarios of increasing scope are considered to assess the influence of coverage on performance. Sufficient semantic knowledge about the task domain is found to be captured at a level of coverage as low as 70%. This illustrates the good generalization properties of semantic inference.

An approach is presented for the dual verification of speaker identity and verbal content in a text-dependent voice authentication system. The application considered is desktop voice login over a far-field microphone. Each speaker is allowed to select his or her own keyphrase, and enrollment is limited to four instances of the keyphrase, each 1 to 2 seconds of speech. The approach decouples the analysis of speaker and verbal content information, so as to use two light-weight components for verification: a spectral matching component based on a global representation of the entire utterance, and a temporal alignment component based on more conventional frame-level information. The resulting integration is language-independent, and experiments with deliberate imposture show an equal error rate figure of approximately 4%. The approach has been commercially deployed in the "VoicePrint Password" feature of MacOS 9.

Apple's next-generation text-to-speech (TTS) system in MacOS X uses a superpositional pitch model, comprising a relatively smooth underlying F0 contour and a separate contribution from the in uence of the phonetic segments. This paper focuses on the data-driven modelling of the underlying contour, based on electroglottographic signals obtained from a corpus of reiterant speech. F0 extraction from such signals leads to more accurate characteristic shapes, as objectively illustrated by a typically low mean absolute frequency deviation (between 2 and 3 Hz) between original and synthetic F0 contours. This in turn supports a better (both more complete and more realistic) model of F0 behavior. Experimental results illustrate the improved prosodic representation resulting from this F0 model.

The increasing availability of carefully designed and collected speech corpora opens up new possibilities for the statistical estimation of formal multivariate prosodic models. At Apple Computer, statistical prosodic modeling exploits the Victoria corpus, created to broadly support ongoing speech synthesis research and development. This corpus is composed of five constituent parts, each designed to cover a specific aspect of speech synthesis: polyphones, prosodic contexts, reiterant speech, function word sequences, and continuous speech. This paper focuses on the use of the Victoria corpus in the statistical estimation of duration and pitch models for Apple's next-generation text-to-speech system in Macintosh OS X. Duration modeling relies primarily on the subcorpus of prosodic contexts, which is instrumental to uncover empirical evidence in favor of a piecewise linear transformation in the well-known sums-of-products approach. Pitch modeling relies primarily on the subcorpus of reiterant speech, which makes possible the optimization of superpositional pitch models with more accurate underlying smooth contours. Experimental results illustrate the improved prosodic representation resulting from these new duration and pitch models.

The "sums-of-products" approach has emerged as one of the most promising avenues to model contextual influences on phoneme duration. The associated regression is generally applied after log-transforming the durations. This paper presents empirical and theoretical evidence which suggests that this transformation is not optimal. A promising alternative solution is proposed, based on a sigmoid function. Preliminary experimental results obtained on over 50,000 phonemes in varied prosodic contexts show that this transformation reduces the unexplained deviations in the data by more than 30%. Alternatively, for a given level of performance, it halves the number of parameters required by the model.

Some Single-Microphone Speech Enhancement algorithms based on the iterative Wiener filtering Method due to Lim-Oppenheim [2] are evaluated. In the original Lim-Oppenheim algorithm, AR spectral estimation of speech is carried out using a second-order analysis, but our algorithms consider an AR estimation from a cumulant analysis. This work extends some preceding papers due to the authors [4], [5]. Third- and fourth-order cumulant-based algorithms are compared to classical second-order one. This comparison is evaluated by considering three different noisy environments. A detailed study based on a frame-by-frame analysis leads to an optimum iteration of each algorithm as a trade-off between noise reduction and distortion effects. Voiced and unvoiced sounds are separately discussed. We conclude that third-order cumulant-based algorithm offers a more valuable performance than the others.