44 citations · 103 across the 10 of their papers we have counts for
13 papers
Robust Summarization of Doctor-Patient Conversations: TalTech Systems for the Beyond Transcription Challenge
Aivo Olev, Tanel Alumäe
This paper describes TalTech's submissions to the Beyond Transcription Challenge (BeTraC), which requires generating SOAP notes directly from long doctor-patient conversation recor…
TalTech-IRIT-LIS Speaker and Language Diarization Systems for DISPLACE 2024
Joonas Kalda, Tanel Alumäe, Martin Lebourdais +3
This paper describes the submissions of team TalTech-IRIT-LIS to the DISPLACE 2024 challenge. Our team participated in the speaker diarization and language diarization tracks of th…
Finetuning End-to-End Models for Estonian Conversational Spoken Language Translation
Tiia Sildam, Andra Velve, Tanel Alumäe
This paper investigates the finetuning of end-to-end models for bidirectional Estonian-English and Estonian-Russian conversational speech-to-text translation. Due to the limited av…
PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings
Joonas Kalda, Clément Pagés, Ricard Marxer +2
A major drawback of supervised speech separation (SSep) systems is their reliance on synthetic data, leading to poor real-world generalization. Mixture invariant training (MixIT) w…
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge
Tanel Alumäe, Jiaming Kong, Daniil Robnikov
This paper describes Tallinn University of Technology (TalTech) systems developed for the ASRU MADASR 2023 Challenge. The challenge focuses on automatic speech recognition of diale…
Collar-aware Training for Streaming Speaker Change Detection in Broadcast Speech
Joonas Kalda, Tanel Alumäe
In this paper, we present a novel training method for speaker change detection models. Speaker change detection is often viewed as a binary sequence labelling problem. The main cha…
Pretraining Approaches for Spoken Language Recognition: TalTech Submission to the OLR 2021 Challenge
Tanel Alumäe, Kunnar Kukk
This paper investigates different pretraining approaches to spoken language identification. The paper is based on our submission to the Oriental Language Recognition 2021 Challenge…
Improving Language Identification of Accented Speech
Kunnar Kukk, Tanel Alumäe
Language identification from speech is a common preprocessing step in many spoken language processing systems. In recent years, this field has seen fast progress, mostly due to the…
VoxLingua107: a Dataset for Spoken Language Recognition
Jörgen Valk, Tanel Alumäe
This paper investigates the use of automatically collected web audio data for the task of spoken language recognition. We generate semi-random search phrases from language-specific…
Robust Training of Vector Quantized Bottleneck Models
Adrian Łańcucki, Jan Chorowski, Guillaume Sanchez +6
In this paper we demonstrate methods for reliable and efficient training of discrete representation using Vector-Quantized Variational Auto-Encoder models (VQ-VAEs). Discrete laten…
Advanced Rich Transcription System for Estonian Speech
Tanel Alumäe, Ottokar Tilk, Asadullah
This paper describes the current TTÜ speech transcription system for Estonian speech. The system is designed to handle semi-spontaneous speech, such as broadcast conversations, lec…
Weakly Supervised Training of Speaker Identification Models
Martin Karu, Tanel Alumäe
We propose an approach for training speaker identification models in a weakly supervised manner. We concentrate on the setting where the training data consists of a set of audio re…
Low-Resource Neural Headline Generation
Ottokar Tilk, Tanel Alumäe
Recent neural headline generation models have shown great results, but are generally trained on very large datasets. We focus our efforts on improving headline quality on smaller d…