11 citations · 21 across the 15 of their papers we have counts for
18 papers
VākQA: A Benchmark and Evaluation Study for Telugu Spoken Factoid Question Answering
Bhavana Akkiraju, Ravi Sastry Kolluru, Sri Charan D +3
Question answering has advanced rapidly with large language models, but predominantly for high-resource languages, in both text and spoken settings. Spoken question answering (SQA)…
S-DiverSe: Spanish Diverse Speech
Fernando López, Fernando Ibañez, Ana Martínez +4
Automatic speech recognition (ASR) has advanced remarkably for standard speech, yet speech affected by neurological conditions remains a challenge. We present S-DiverSe (Spanish Di…
FiLM-Based Speaker Conditioning of a SpeechLLM for Pathological Speech Recognition
Fernando López, Santosh Kesiraju, Jordi Luque
Automatic speech recognition (ASR) has advanced remarkably for standard speech; however, pathological speech from neurological conditions remains a significant challenge. We invest…
FLiP: Towards understanding and interpreting multimodal multilingual sentence embeddings
Santosh Kesiraju, Bolaji Yusuf, Šimon Sedláček +2
This paper presents factorized linear projection (FLiP) models for understanding pretrained sentence embedding spaces. We train FLiP models to recover the lexical content from mult…
DeCRED: Decoder-Centric Regularization for Encoder-Decoder Based Speech Recognition
Alexander Polok, Santosh Kesiraju, Karel Beneš +3
This paper presents a simple yet effective regularization for the internal language model induced by the decoder in encoder-decoder ASR models, thereby improving robustness and gen…
End-to-End Speech Translation for Low-Resource Languages Using Weakly Labeled Data
Aishwarya Pothula, Bhavana Akkiraju, Srihari Bandarupalli +3
The scarcity of high-quality annotated data presents a significant challenge in developing effective end-to-end speech-to-text translation (ST) systems, particularly for low-resour…
Factors affecting the in-context learning abilities of LLMs for dialogue state tracking
Pradyoth Hegde, Santosh Kesiraju, Jan Švec +5
This study explores the application of in-context learning (ICL) to the dialogue state tracking (DST) problem and investigates the factors that influence its effectiveness. We use…
Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs
Šimon Sedláček, Bolaji Yusuf, Ján Švec +4
In this work, we approach spoken Dialogue State Tracking (DST) by bridging the representation spaces of speech encoders and LLMs via a small connector module, with a focus on fully…
IIITH-BUT system for IWSLT 2025 low-resource Bhojpuri to Hindi speech translation
Bhavana Akkiraju, Aishwarya Pothula, Santosh Kesiraju +1
This paper presents the submission of IIITH-BUT to the IWSLT 2025 shared task on speech translation for the low-resource Bhojpuri-Hindi language pair. We explored the impact of hyp…
Aligning Pre-trained Models for Spoken Language Translation
Šimon Sedláček, Santosh Kesiraju, Alexander Polok +1
This paper investigates a novel approach to end-to-end speech translation (ST) based on aligning frozen pre-trained automatic speech recognition (ASR) and machine translation (MT)…
Improving Automatic Speech Recognition with Decoder-Centric Regularisation in Encoder-Decoder Models
Alexander Polok, Santosh Kesiraju, Karel Beneš +2
This paper proposes a simple yet effective way of regularising the encoder-decoder-based automatic speech recognition (ASR) models that enhance the robustness of the model and impr…
Beyond the Labels: Unveiling Text-Dependency in Paralinguistic Speech Recognition Datasets
Jan Pešán, Santosh Kesiraju, Lukáš Burget +1
Paralinguistic traits like cognitive load and emotion are increasingly recognized as pivotal areas in speech recognition research, often examined through specialized datasets like…
Strategies for improving low resource speech to text translation relying on pre-trained ASR models
Santosh Kesiraju, Marek Sarvas, Tomas Pavlicek +2
This paper presents techniques and findings for improving the performance of low-resource speech to text translation (ST). We conducted experiments on both simulated and real-low r…
Detecting English Speech in the Air Traffic Control Voice Communication
Igor Szoke, Santosh Kesiraju, Ondrej Novotny +3
We launched a community platform for collecting the ATC speech world-wide in the ATCO2 project. Filtering out unseen non-English speech is one of the main components in the data pr…
Rethinking the Objectives of Extractive Question Answering
Martin Fajcik, Josef Jon, Pavel Smrz
This work demonstrates that using the objective with independence assumption for modelling the span probability of span starting at position and e…
A Bayesian Multilingual Document Model for Zero-shot Topic Identification and Discovery
Santosh Kesiraju, Sangeet Sagar, Ondřej Glembek +3
In this paper, we present a Bayesian multilingual document model for learning language-independent document embeddings. The model is an extension of BaySMM [Kesiraju et al 2020] to…
Learning document embeddings along with their uncertainties
Santosh Kesiraju, Oldřich Plchot, Lukáš Burget +1
Majority of the text modelling techniques yield only point-estimates of document embeddings and lack in capturing the uncertainty of the estimates. These uncertainties give a notio…
An Empirical Evaluation of Zero Resource Acoustic Unit Discovery
Chunxi Liu, Jinyi Yang, Ming Sun +7
Acoustic unit discovery (AUD) is a process of automatically identifying a categorical acoustic unit inventory from speech and producing corresponding acoustic unit tokenizations. A…