activity
20182026
most citedTowards Consistent Hybrid HMM Acoustic Modeling

4 citations · 7 across the 14 of their papers we have counts for

collaborators

18 papers

cs.CL2026

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…

cs.CL2026

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…

cs.FL2026

Fast and General Automatic Differentiation for Finite-State Methods

Lucas Ondel Yang, Tina Raissi, Martin Kocour +2

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…

cs.CL2025

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…

cs.SD2025

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…

cs.SD2025

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…