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Colloque avec actes et comité de lecture. internationale.; International audience; We present a novel noise compensation architecture which makes no assumptions on how the noise sources alter the speech data and which do not rely on clean speech models. Rather, this new architecture makes the (realistic) assumption that speech databases recorded under different background noise conditions are available. Its main principle is to process individually each database and to construct a parametric representation which describes the variation of acoustic models w.r.t. noise models. This representation is then used during recognition to estimate the acoustic models in the new environment. We evaluate the performance of this new compensation scheme on a connected digits recognition task and show that it can perform significantly better than multi-conditions training, which is the most widely used technique in these kind of scenarios.

Colloque avec actes et comité de lecture. internationale.; International audience; In this paper we propose a new approach to language modeling based on dynamic Bayesian networks. The principle idea of our approach is to find the dependence relations between variables that represent different linguistic units (word, class, concept, ...) that constitutes a language model. In the context of this paper the linguistic units that we consider are syntactic classes and words. Our approach should not be considered as a model combination technique. Rather, it is an original and coherent methodology that processes words and classes in the same model. We attempt to identify and model the dependence of words and classes on their linguistic context. Our ultimate goal is to devise an automatic mechanism that extracts the best dependence relations between a word and its context, i.e., lexical and syntactic. Preliminary results are very encouraging, in particular the model in which a word depends not only on previous word but also on syntactic classes of two previous words. This model outperforms the bi-gram model.

Colloque avec actes et comité de lecture. internationale.; International audience; In this paper we propose a new approach to language modeling based on dynamic Bayesian networks. The principle idea of our approach is to find the dependence relations between variables that represent different linguistic units (word, class, concept, ...) that constitutes a language model. In the context of this paper the linguistic units that we consider are syntactic classes and words. Our approach should not be considered as a model combination technique. Rather, it is an original and coherent methodology that processes words and classes in the same model. We attempt to identify and model the dependence of words and classes on their linguistic context. Our ultimate goal is to devise an automatic mechanism that extracts the best dependence relations between a word and its context, i.e., lexical and syntactic. Preliminary results are very encouraging, in particular the model in which a word depends not only on previous word but also on syntactic classes of two previous words. This model outperforms the bi-gram model.

Colloque avec actes et comité de lecture. internationale.; International audience; We present a new continuous automatic speech recognition system where no a priori assumptions on the dependencies between the observed and the hidden speech processes are made. Rather, dependencies are learned form data using the Bayesian networks formalism. This approach guaranties to improve modelling fidelity as compared to HMMs. Furthermore, our approach is technically very attractive because all the computational effort is made in the training phase.

oz GÜRÜLTÜLÜ KONUŞMANIN KALMAN SÜZGECİ İLE İYİLEŞTİRİLMESİ Deviren, Murat Yüksek Lisans, Elektrik ve Elektronik Mühendisliği Bölümü Tez Yöneticisi: Assoc. Prof. Dr. Tolga Çiloğlu Ortak Tez Yöneticisi: Prof. Dr. Mete Severcan Eylül 2000, 55 sayfa Bu çalışma, gürültülü konuşmanın anlaşılabilirliğini arttıracak bir iyileştirme al goritmasının geliştirilmesini amaçlamaktadır. Hem renkli hem de beyaz gürültü etkisi için geçerli olan, parametrik sinyal modeline dayalı bir algoritma gelişti rilmiştir. Konuşmanın seslilik yapısını farklı frekans bantlarında öne çıkartan bir konuşma modeli oluşturulmuştur. Konuşmanın uyarım sinyali peryodik ve gürültülü sinyal karışımı olarak ele alınmış ve karışım oranları farklı bant aralıkları için ayrı ayrı hesaplanmıştır. Önerilen uyarım sinyal modeli ile konuşma için bir durum uzayı modeli oluşturulmuştur. Varsayılan model kullanılarak bir Kalman süzgeci tasarlanmış ve gürültülü gözlemlerden, temiz konuşma kestirilmeye ça-lışılmıştır. Sonuçlar, Kalman süzgeci ile iyileştirme yapan diğer algoritmalarla karşılaştırılmış, modellemenin ve kullanılan filtreleme algoritmasının etkileri tar tışılmıştır. Anahtar Kelimeler: Kalman süzgeci, konuşma iyileştirme vı ABSTRACT ENHANCEMENT OF NOISY SPEECH UTILIZING THE KALMAN FILTER Deviren, Murat M.S., Department of Electrical and Electronics Engineering Supervisor: Assoc. Prof. Dr. Tolga Çiloğlu Co-Supervisor: Prof. Dr. Mete Severcan September 2000, 55 pages The aim of this work is to develop an enhancement algorithm to increase the intelligibility of noisy speech. A parametric model based time domain algorithm is developed to filter out both white and colored noise interference. We devise a speech model that emphasizes the voicing characteristics in different frequency intervals. Excitation signal for speech is modelled as a mixture of periodic and white noise signals where the mixture proportions are calculated separately for different frequency intervals. Based on the proposed excitation model, we formu late a state space model for speech. The presumed speech model is then used to construct an optimal Kalman filter for estimation of clean speech from the noisy mobservations. The results are compared to other Kalman filter based speech en hancement algorithms. The effect of modelling and type of filtering is discussed. Keywords: Kalman filter, speech enhancement IV 55