The Turkish language belongs to the Turkic family. All members of this family are close to one another in terms of linguistic structure. Typological similarities are vowel harmony, verb-final word order and agglutinative morphology. This latter property causes a very fast vocabulary growth resulting in a large number of out-of-vocabulary words. In this paper we describe our first experiments in a speaker independent LVCSR engine for Modern Standard Turkish. First results on our Turkish speech recognition system are presented. The currently best system shows very promising results achieving 16.9% word error rate. To overcome the OOV-problem we propose a morphem-based and the Hypothesis Driven Lexical Adaptation approach. The final Turkish system is integrated into the multilingual recognition engine of the GlobalPhone project.

Building modern speech and language systems currently requires large data resources such as texts, voice recordings, pronunciation lexicons, morphological decomposition information and parsing grammars. Based on a study of the most important differences between language groups, we introduce approaches to efficiently deal with the enormous task of covering even a small percentage of the world's languages. For speech recognition, we have reduced the resource requirements by applying acoustic model combination, bootstrapping and adaption techniques. Similar algorithms have been applied to improve the recognition of foreign accents. Segmenting language into appropriate units reduces the amount of data required to robustly estimate statistical models. The underlying morphological principles are also used to automatically adapt the coverage of our speech recognition dictionaries with the Hypothesis-Driven Lexical Adaptation (HDLA) algorithm. This reduces the out-of-vocabulary problems encountered in agglutinative languages. Speech recognition results are reported for the read GlobalPhone database and some broadcast news data. For speech translation, using a task-oriented Interlingua allows to build a system with N languages with linear, rather than quadratic effort. We have introduced a modular grammar design to maximize reusability and portability. End-to-end translation results are reported on a travel-domain task in the framework of C-STAR.

Adapting the vocabulary of a speech recognizer to the utterance to be recognized has proven to be successful both in reducing high out-of-vocabulary as well as word error rates. This applies especially to languages that have a rapid vocabulary growth due to a large number of inflections and composita. This paper presents various adaptation methods within the hypothesis driven lexical adaptation (HDLA) framework which allow speech recognition on a virtually unlimited vocabulary. Selection criteria for the adaptation process are either based on morphological knowledge or distance measures at phoneme or grapheme level. Different methods are introduced for determining distances between phoneme pairs and for creating the large fallback lexicon the adapted vocabulary is chosen from. HDLA reduces the out-of-vocabulary-rate by 55% for Serbo-Croatian, 35% for German and 27% for Turkish. The reduced out-of-vocabulary rate also decreases the word error rate by an absolute 4.1% to 25.4% on Serbo-Croatian broadcast news data.

One of the most prevailing problems of large-vocabulary speech recognition systems is the large number of out-of-vocabulary words. This is especially the case for automatically transcribing broadcast news in languages other than English, that have a large number of inflections and compound words. We introduce a set of techniques to decrease the number of out-of-vocabulary words during recognition by using linguistic knowledge about morphology and a two-pass recognition approach, where the first pass only serves to dynamically adapt the recognition dictionary to the speech segment to be recognized. A second recognition run is then carried out on the adapted vocabulary. With the proposed techniques we were able to reduce the OOV-rate by more than 40% thereby also improving the recognition results by an absolute 5.8% from a 64% word accuracy to 69.8%.

This paper describes the development of a Serbo-Croatian dictation and broadcast news speech recognizer. The intention is to generate an automatic text transcription of a news show, which will be submitted to a multilingual informedia database. We outline the complete system development process using the JanusRTk, beginning with data collection, design and training of the parameters, tuning and evaluation. We report on general recognition techniques like segmentation, adaptation and language model interpolation, as well as language specific problems, e.g. high OOV rate due to inflected word forms. We show that even with a low amount of acoustic training data, combined with Web based interpolated language models, it is sufficient to build up a fairly reliable automatic news transcription system, which yields a performance of 36.0% word error (WE).

This paper describes our latest efforts in building a speech recognizer for operating a navigation system through speech instead of typed input. Compared to conventional speech recognition for navigation systems, where the input is usually restricted to a fixed set of keywords and keyword phrases, complete spontaneous sentences are allowed as speech input. We will present the interaction of speech input, parsing and the necessary reactions to the requested queries. Our system has been trained on German spontaneous speech data and has been adapted to navigation queries using MLLR. As the system is not restricted to command word input, a parser is necessary to further process the recognized utterance. We show that within a lab environment our system is able to handle arbitrary spontaneous sentences as input to a navigation system successfully. The performance of the recognizer measured in word error rate gives a result of 18%. The parser has also been evaluated and yields an error rate o...

High out-of-vocabulary (OOV) rates are one of the most prevailing problems for languages with a rapid vocabulary growth due to a large number of inflections. Especially when transcribing SerboCroatian and German broadcast news, the OOV-rate is between 8.7% and 4.5%. Hypothesis Driven Lexical Adaptation (HDLA) has already been shown to decrease high OOV-rates significantly by using morphology-based linguistic knowledge. This paper introduces another approach to dynamically adapt a recognition lexicon to the utterance to be recognized. Instead of morphological knowledge about word stems and inflection endings, distance measures based on Levenstein distance are used. Results based on phoneme and grapheme distances will be presented. Compared to the use of morphological knowledge, our distance-based approach offers the distinct advantage that no expert knowledge about a specific language is required, no definition of complex grammar rules is necessary. Instead, grapheme sequences or the ph...

The recognition of broadcast news is a challenging problem in speech recognition. To achieve the long-term goal of robust, real-time news transcription, several problems have to be overcome, e.g. the variety of acoustic conditions and the unlimited vocabulary. Recently, a number of sites have been working on content-addressable multi-media information sources. In the presented paper, we focus on extending this work towards a multi-lingual environment, where queries and multimedia documents may appear in multiple languages. In cooperation with the Informedia project at CMU [4], we attempt to provide cross-lingual access to German and Serbo-Croatian newscasts. 1. THE VIEW4YOU SYSTEM In the View4You system, German and Serbocroatian public newscasts are recorded daily using standard consumer electronics equipment. The newscasts are automatically segmented and an index is created for each of the segments by means of automatic speech recognition. The user can query the system in natural lan...

Verbmobil, a German research project, aims at machine translation of spontaneous speech input. The ultimate goal is the development of a portable machine translator that will allow people to negotiate in their native language. Within this project the University of Karlsruhe has developed a speech recognition engine that has been evaluated on a yearly basis during the project and shows very promising speech recognition word accuracy results on large vocabulary spontaneous speech. We introduce the Janus Speech Recognition Toolkit underlying the speech recognizer. The main new contributions to the acoustic modeling part of our 1996 evaluation system-speaker normalization, channel normalization and polyphonic clustering-are discussed and evaluated. Besides the acoustic models we delineate the different language models used in our evaluation system: word trigram models interpolated with class based models and a separate spelling language model were applied. As a result of using the toolkit and integrating all these parts into the recognition engine the word error rate on the German spontaneous scheduling task (GSST) could be decreased from 30% word error rate in 1995 to 13.8% in 1996.

Current speech recognition systems usually use word-based trigram language models. More elaborate models are applied to word lattices or N best lists in a rescoring pass following the acoustic decoding process. In this paper we consider techniques for dealing with class-based language models in the lattice rescoring framework of our JANUS large vocabulary speech recognizer. We demonstrate how to interpolate with a Part-of-Speech (POS) tag-based language model as example of a class-based model, where a word can be member of many different classes. Here the actual class membership of a word in the lattice becomes a hidden event of the A algorithm used for rescoring. A forward type of algorithm is defined as extension of the lattice rescorer to handle these hidden events in a mathematically sound fashion. Applying the mixture of viterbi and forward kind of rescoring procedure to the German Spontaneous Scheduling Task (GSST) yields some improvement in word accuracy. Above all, the resc...

Building robust stochastic language models is a major issue in speech recognition systems.Conventional word-based n-gram models do not capture any linguistic constraints inherent in speech.In this paper the notion of function and content w ords open closed word classes is used to provide linguistic knowledge that can be incorporated into language models.Function words are articles, prepositions, personal pronouns content w ords are nouns, verbs, adjectives and adverbs.Based on this class de nition resulting in function and content w ord markers, a new language model is de ned.A combination of the word-based model with this new model will be introduced.The combined model shows modest improvements both in perplexity results and recognition performance.

To guarantee unrestricted natural language processing, state-of-the-art speech recognition systems require huge dictionaries that increase search space and result in performance degradations. This is especially true for languages where there do exist a large number of inflections and compound words such as German, Spanish, etc. One way to keep up decent recognition results with increasing vocabulary is the use of other base units than simply words. Different decomposition methods originally based on morphological decomposition for the German language are compared. Not only do they counteract the immense vocabulary growth with an increasing amount of training data, also the rate of out-of-vocabulary words, which worsens recognition performance significantly in German, is decreased. A smaller dictionary also leads to 30 K speed improvement during the recognition process. Moreover even if the amount of available training data is quite huge it is often not enough to guarantee robust language model estimations, whereas morphem-based models are capable to do so.