5 citations · 8 across the 10 of their papers we have counts for
13 papers
Word Timestamps and Speaker Attribution with a Non-Autoregressive LLM
Zvi Kons, Avihu Dekel, Hagai Aronowitz +2
Timestamps and speaker attribution are useful additions to speech recognition, creating a rich text transcript. This information can either be extracted during transcription or ali…
Speaker Attributed Automatic Speech Recognition Using Speech Aware LLMS
Hagai Aronowitz, Zvi Kons, Avihu Dekel +2
Speaker-Attributed Automatic Speech Recognition (SAA) enhances traditional ASR systems by incorporating relative speaker identity tags directly into the transcript (e.g., [Speaker…
Knowing What to Stress: A Discourse-Conditioned Text-to-Speech Benchmark
Arnon Turetzky, Avihu Dekel, Hagai Aronowitz +2
Spoken meaning often depends not only on what is said, but also on which word is emphasized. The same sentence can convey correction, contrast, or clarification depending on where…
Advancing Speech Understanding in Speech-Aware Language Models with GRPO
Avishai Elmakies, Hagai Aronowitz, Nimrod Shabtay +3
In this paper, we introduce a Group Relative Policy Optimization (GRPO)-based method for training Speech-Aware Large Language Models (SALLMs) on open-format speech understanding ta…
Spoken question answering for visual queries
Nimrod Shabtay, Zvi Kons, Avihu Dekel +3
Question answering (QA) systems are designed to answer natural language questions. Visual QA (VQA) and Spoken QA (SQA) systems extend the textual QA system to accept visual and spo…
Granite-speech: open-source speech-aware LLMs with strong English ASR capabilities
George Saon, Avihu Dekel, Alexander Brooks +21
Granite-speech LLMs are compact and efficient speech language models specifically designed for English ASR and automatic speech translation (AST). The models were trained by modali…
A Non-autoregressive Model for Joint STT and TTS
Vishal Sunder, Brian Kingsbury, George Saon +5
In this paper, we take a step towards jointly modeling automatic speech recognition (STT) and speech synthesis (TTS) in a fully non-autoregressive way. We develop a novel multimoda…
Speech Synthesis From Continuous Features Using Per-Token Latent Diffusion
Arnon Turetzky, Avihu Dekel, Nimrod Shabtay +5
We present SALAD, a zero-shot TTS autoregressive model operating over continuous speech representations. SALAD utilizes a per-token diffusion process to refine and predict continuo…
Extending RNN-T-based speech recognition systems with emotion and language classification
Zvi Kons, Hagai Aronowitz, Edmilson Morais +4
Speech transcription, emotion recognition, and language identification are usually considered to be three different tasks. Each one requires a different model with a different arch…
Towards a Common Speech Analysis Engine
Hagai Aronowitz, Itai Gat, Edmilson Morais +2
Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-super…
Speech Emotion Recognition using Self-Supervised Features
Edmilson Morais, Ron Hoory, Weizhong Zhu +3
Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speec…
Speaker Normalization for Self-supervised Speech Emotion Recognition
Itai Gat, Hagai Aronowitz, Weizhong Zhu +2
Large speech emotion recognition datasets are hard to obtain, and small datasets may contain biases. Deep-net-based classifiers, in turn, are prone to exploit those biases and find…
Siamese x-vector reconstruction for domain adapted speaker recognition
Shai Rozenberg, Hagai Aronowitz, Ron Hoory
With the rise of voice-activated applications, the need for speaker recognition is rapidly increasing. The x-vector, an embedding approach based on a deep neural network (DNN), is…