5 citations · 9 across the 13 of their papers we have counts for
19 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…
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…
Creating an African American-Sounding TTS: Guidelines, Technical Challenges,and Surprising Evaluations
Claudio Pinhanez, Raul Fernandez, Marcelo Grave +2
Representations of AI agents in user interfaces and robotics are predominantly White, not only in terms of facial and skin features, but also in the synthetic voices they use. In t…
Speak While You Think: Streaming Speech Synthesis During Text Generation
Avihu Dekel, Slava Shechtman, Raul Fernandez +3
Large Language Models (LLMs) demonstrate impressive capabilities, yet interaction with these models is mostly facilitated through text. Using Text-To-Speech to synthesize LLM outpu…
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…
A new data augmentation method for intent classification enhancement and its application on spoken conversation datasets
Zvi Kons, Aharon Satt, Hong-Kwang Kuo +4
Intent classifiers are vital to the successful operation of virtual agent systems. This is especially so in voice activated systems where the data can be noisy with many ambiguous…
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…
RNN Transducer Models For Spoken Language Understanding
Samuel Thomas, Hong-Kwang J. Kuo, George Saon +5
We present a comprehensive study on building and adapting RNN transducer (RNN-T) models for spoken language understanding(SLU). These end-to-end (E2E) models are constructed in thr…
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems
Yinghui Huang, Hong-Kwang Kuo, Samuel Thomas +5
Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is…
End-to-End Spoken Language Understanding Without Full Transcripts
Hong-Kwang J. Kuo, Zoltán Tüske, Samuel Thomas +7
An essential component of spoken language understanding (SLU) is slot filling: representing the meaning of a spoken utterance using semantic entity labels. In this paper, we develo…
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…
Controllable Sequence-To-Sequence Neural TTS with LPCNET Backend for Real-time Speech Synthesis on CPU
Slava Shechtman, Carmel Rabinovitz, Alex Sorin +2
State-of-the-art sequence-to-sequence acoustic networks, that convert a phonetic sequence to a sequence of spectral features with no explicit prosody prediction, generate speech wi…
High quality, lightweight and adaptable TTS using LPCNet
Zvi Kons, Slava Shechtman, Alex Sorin +2
We present a lightweight adaptable neural TTS system with high quality output. The system is composed of three separate neural network blocks: prosody prediction, acoustic feature…