9 citations · 21 across the 17 of their papers we have counts for
18 papers
A Native-Reference Phone-Class Geometry for Second-Language Pronunciation Analysis
Tina Raissi, Nhan Phan, Chenxiao Wang +1
Automatic speaking assessment systems can provide holistic proficiency scores, but often lack interpretable measures that characterize pronunciation quality. We propose a native-re…
A Native-Reference Coordinate Geometry for L2 Pronunciation Deviation Using Self-Supervised Speech Models
Tina Raissi, Nhan Phan, Mikko Kurimo
Self-supervised speech models encode rich phonetic information, but it remains unclear how to transform this information into interpretable metrics for second-language (L2) pronunc…
Fast and General Automatic Differentiation for Finite-State Methods
Lucas Ondel Yang, Tina Raissi, Martin Kocour +3
We propose a new method, that we coined the ``morphism-trick'', to integrate custom implementations of vector-Jacobian products in automatic differentiation softwares, applicable t…
Supplementary Resources and Analysis for Automatic Speech Recognition Systems Trained on the Loquacious Dataset
Nick Rossenbach, Robin Schmitt, Tina Raissi +3
The recently published Loquacious dataset aims to be a replacement for established English automatic speech recognition (ASR) datasets such as LibriSpeech or TED-Lium. The main goa…
A Comparative Analysis on ASR System Combination for Attention, CTC, Factored Hybrid, and Transducer Models
Noureldin Bayoumi, Robin Schmitt, Tina Raissi +3
Combination approaches for speech recognition (ASR) systems cover structured sentence-level or word-based merging techniques as well as combination of model scores during beam sear…
Analysis of Domain Shift across ASR Architectures via TTS-Enabled Separation of Target Domain and Acoustic Conditions
Tina Raissi, Nick Rossenbach, Ralf Schlüter
We analyze automatic speech recognition (ASR) modeling choices under domain mismatch, comparing classic modular and novel sequence-to-sequence (seq2seq) architectures. Across the d…
Right Label Context in End-to-End Training of Time-Synchronous ASR Models
Tina Raissi, Ralf Schlüter, Hermann Ney
Current time-synchronous sequence-to-sequence automatic speech recognition (ASR) models are trained by using sequence level cross-entropy that sums over all alignments. Due to the…
Investigating the Effect of Label Topology and Training Criterion on ASR Performance and Alignment Quality
Tina Raissi, Christoph Lüscher, Simon Berger +2
The ongoing research scenario for automatic speech recognition (ASR) envisions a clear division between end-to-end approaches and classic modular systems. Even though a high-level…
End-to-End Training of a Neural HMM with Label and Transition Probabilities
Daniel Mann, Tina Raissi, Wilfried Michel +2
We investigate a novel modeling approach for end-to-end neural network training using hidden Markov models (HMM) where the transition probabilities between hidden states are modele…
Competitive and Resource Efficient Factored Hybrid HMM Systems are Simpler Than You Think
Tina Raissi, Christoph Lüscher, Moritz Gunz +2
Building competitive hybrid hidden Markov model~(HMM) systems for automatic speech recognition~(ASR) requires a complex multi-stage pipeline consisting of several training criteria…
HMM vs. CTC for Automatic Speech Recognition: Comparison Based on Full-Sum Training from Scratch
Tina Raissi, Wei Zhou, Simon Berger +2
In this work, we compare from-scratch sequence-level cross-entropy (full-sum) training of Hidden Markov Model (HMM) and Connectionist Temporal Classification (CTC) topologies for a…
Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech
Christoph Lüscher, Mohammad Zeineldeen, Zijian Yang +6
Language barriers present a great challenge in our increasingly connected and global world. Especially within the medical domain, e.g. hospital or emergency room, communication dif…
Improving Factored Hybrid HMM Acoustic Modeling without State Tying
Tina Raissi, Eugen Beck, Ralf Schlüter +1
In this work, we show that a factored hybrid hidden Markov model (FH-HMM) which is defined without any phonetic state-tying outperforms a state-of-the-art hybrid HMM. The factored…
Towards Consistent Hybrid HMM Acoustic Modeling
Tina Raissi, Eugen Beck, Ralf Schlüter +1
High-performance hybrid automatic speech recognition (ASR) systems are often trained with clustered triphone outputs, and thus require a complex training pipeline to generate the c…
Improved Robustness to Disfluencies in RNN-Transducer Based Speech Recognition
Valentin Mendelev, Tina Raissi, Guglielmo Camporese +1
Automatic Speech Recognition (ASR) based on Recurrent Neural Network Transducers (RNN-T) is gaining interest in the speech community. We investigate data selection and preparation…
Context-Dependent Acoustic Modeling without Explicit Phone Clustering
Tina Raissi, Eugen Beck, Ralf Schlüter +1
Phoneme-based acoustic modeling of large vocabulary automatic speech recognition takes advantage of phoneme context. The large number of context-dependent (CD) phonemes and their h…
Sample Drop Detection for Distant-speech Recognition with Asynchronous Devices Distributed in Space
Tina Raissi, Santiago Pascual, Maurizio Omologo
In many applications of multi-microphone multi-device processing, the synchronization among different input channels can be affected by the lack of a common clock and isolated drop…
Extended pipeline for content-based feature engineering in music genre recognition
Tina Raissi, Alessandro Tibo, Paolo Bientinesi
We present a feature engineering pipeline for the construction of musical signal characteristics, to be used for the design of a supervised model for musical genre identification.…