Speech Analytics

May 30, 1999 | Projects

- PROJECT CLOSED

The project includes technology transfer activities from FBK-irst to Pervoice and development activities such as improvements of automatic transcription technology (rich transcription, automatic text polishing), speech analytics technologies for call centers (emotional state recognition, segmentation and classification of utterances, monitoring of transactions), and advanced acoustic normalization techniques.

MT Group at FBK Follow

#MachineTranslation Research Unit @FBK_research. #nlproc #deeplearning #ai

Avatar MT Group at FBK @fbk_mt ·

10 Jun

Our pick of the week by
@lina_conti

: "Greater accessibility can amplify discrimination in generative AI" by
@CarolinHolterm, @minhducbui_nlp, @KaitlynZhou, @vjhofmann, @kelina1124, @anne_lauscher
📰
#GenderBias #SpeechLLM

Lina Conti @lina_conti

Pick of the week @fbk_mt: "Greater accessibility can amplify discrimination in generative AI"

Gender bias in speech-based LLMs examined from multiple angles: a user survey, automatic bias measurement, and pitch manipulation experiments.

https://arxiv.org/pdf/2603.22260

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Avatar MT Group at FBK @fbk_mt ·

9 Jun

Late update, but we had two great talks last month!

#MachineTranslation #FBK #NLProc #GenderBias #SpeechSynthesis

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Avatar MT Group at FBK @fbk_mt ·

3 Jun

Our pick of the week by @dhairya_su47605

: "Scaling Laws for Precision" by @tanishqkumar07, Zachary Ankner, @bfspectorShiekh, @blake__bordelon, @Muennighoff, @mansiege, @CPehlevan, Christopher R´e, @AdtRaghunathan

📰

#Quantization #LLM #ScalingLaw

Dhairya Suman @dhairya_su47605

Pick of the week @fbk_mt
Super interesting paper on the limitations of quantization, demonstrating how post-training quantization scales poorly in data.

https://arxiv.org/abs/2411.04330

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Avatar MT Group at FBK @fbk_mt ·

27 May

⭐ For our #PickOfTheWeek, this paper explores an important question for modern speech AI:

🎙️ Which Evaluation for Which Speech Model?
👥 Authors: @Maureendss , @EeshanDhekane

Speech foundation models are evolving rapidly, but evaluation practices are still fragmented.

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