Eduardo Rodríguez Banga

Hierarchical phrase-based translation with weighted finite-state transducers and shallow-n grammars

In this article we describe HiFST, a lattice-based decoder for hierarchical phrase-based translation and alignment. The decoder is implemented with standard Weighted Finite-State Transducer (WFST) operations as an alternative to the well-known cube pruning procedure. We find that the use of WFSTs rather than k-best lists requires less pruning in translation search, resulting in fewer search errors, better parameter optimization, and improved translation performance. The direct generation of translation lattices in the target language can improve subsequent rescoring procedures, yielding further gains when applying long-span language models and Minimum Bayes Risk decoding. We also provide insights as to how to control the size of the search space defined by hierarchical rules. We show that shallow-n grammars, low-level rule catenation, and other search constraints can help to match the power of the translation system to specific language pairs.

Rule Filtering by Pattern for Efficient Hierarchical Translation

We describe refinements to hierarchical translation search procedures intended to reduce both search errors and memory usage through modifications to hypothesis expansion in cube pruning and reductions in the size of the rule sets used in translation. Rules are put into syntactic classes based on the number of non-terminals and the pattern, and various filtering strategies are then applied to assess the impact on translation speed and quality. Results are reported on the 2008 NIST Arabic-to-English evaluation task.

Hierarchical Phrase-Based Translation with Weighted Finite State Transducers

This paper describes a lattice-based decoder for hierarchical phrase-based translation. The decoder is implemented with standard WFST operations as an alternative to the well-known cube pruning procedure. We find that the use of WFSTs rather than k-best lists requires less pruning in translation search, resulting in fewer search errors, direct generation of translation lattices in the target language, better parameter optimization, and improved translation performance when rescoring with long-span language models and MBR decoding. We report translation experiments for the Arabic-to-English and Chinese-to-English NIST translation tasks and contrast the WFST-based hierarchical decoder with hierarchical translation under cube pruning.

A method for combining intonation modelling and speech unit selection in corpus-based speech synthesis systems

In this paper, we focus on improving the quality of corpus-based synthesis systems by considering several candidate intonation contours. These candidates are generated by a unit selection procedure for which cost functions are defined. The consideration of several possible pitch contours adds an additional degree of freedom to the search for appropriate speech units. Objective and subjective tests confirm an improvement in the quality of the resulting synthetic speech.

Integrating phrasing and intonation modelling using syntactic and morphosyntactic information

This paper focuses on the relationship between intonation and syntactic and morphosyntactic information. Although intonation and syntax are both related to dependencies between the different parts of a sentence, and are therefore related to meaning, the precise influence of grammar on intonation is not clear. We describe a novel method that uses syntactic and part-of-speech features in the framework of corpus-based intonation modelling, and which integrates part of the phrasing algorithm in the unit selection stage. Subjective tests confirm an improvement in the quality of the resulting synthetic intonation: 75% of the sentences synthesised with the new intonation model were considered to be better or much better than the sentences synthesised using the old model, while only 7.5% of sentences were rated as worse or much worse.

Specific features of the Galician language and implications for speech technology development

In this article, we present the main linguistic and phonetic features of Galician which need to be considered in the development of speech technology applications for this language. We also describe the solutions adopted in our text-to-speech system, also useful for speech recognition and speech-to-speech translation. On the phonetic plane in particular, the handling of vocal contact and the determination of mid-vowel openness are discussed. On the linguistic plane we place special emphasis on the handling of clitics and verbs. It should be noted that in Galician there is high interrelation between phonetics and grammatical information. Therefore, the task of morphosyntactic disambiguation is also addressed. Moreover, this task is fundamental for a higher level linguistic analysis.

Piecewise linear definition of transformation functions for speaker de-identification

The main drawback of speaker de-identification approaches using voice conversion techniques is the need for parallel corpora to train transformation functions between the source and target speakers. In this paper, a voice conversion approach that does not require training any parameters is proposed: it consists in manually defining frequency warping (FW) based transformations by using piecewise linear approximations. An analysis of the de-identification capabilities of the proposed approach using FW only or combined with FW modification and spectral amplitude scaling (AS) was performed. Experimental results show that, using the manually defined transformations using only FW, it is not possible to obtain de-identified natural sounding speech. Nevertheless, when modifying the FW, both de-identification accuracy and naturalness increase to a great extent. A slight improvement in de-identification was also obtained when applying spectral amplitude scaling.

Language-independent acoustic cloning of HTS voices: A preliminary study

This paper presents a new method for cross-lingual speaker adaptation in the framework of HMM-based speech synthesis. Taking two HTS voice models as input, one for the desired language and another for the aimed speaker identity, it yields a third model that produces speech in the target language while sounding like the target speaker. The method operates at segmental level (spectral information and average fundamental frequency) and does not require any phonetic or linguistic information. Perceptual evaluation experiments show that, when the input models are good enough, the resulting synthetic voice is perceived as similar to the target speaker with no important quality degradation.

TelCorreo: A Bilingual E-mail Client over the Telephone

In this paper we present TelCorreo [1]: an e-mail client that allows the Galician Internet users to read their e-mail messages from locations where there is no web-connected computer available. This task is performed using speech through the telephone, and the system uses speech technology developed in Galician, including a speech recognition system and a text-to-speech converter.

Language-Independent Acoustic Cloning of HTS Voices: An Objective Evaluation

In a previous work we presented a method to combine the acoustic characteristics of a speech synthesis model with the linguistic characteristics of another one. This paper presents a more extensive evaluation of the method when applied to cross-lingual adaptation. A large number of voices from a database in Spanish are adapted to Basque, Catalan, English and Galician. Using a state-of-the-art speaker identification system, we show that the proposed method captures the identity of the target speakers almost as well as standard intra-lingual adaptation techniques.