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Internship - Machine Learning Research - Speech-to-Speech Translation

Apple

Skills

Required

  • speech recognition / speech synthesis / machine translation / NLP / multimodal learning
  • deep learning toolkits
  • PyTorch / JAX / TensorFlow
  • programming
  • Python

Preferred

  • speech-to-speech / speech-to-text translation
  • speech generation
  • text-to-speech or
  • neural TTS / speech tokenizers / audio codecs / expressive and
  • multilingual synthesis
  • fine-tuning
  • multimodal (audio + text) models
  • speech and translation evaluation
  • WER

The Machine Translation and Multilingual AI team is looking for research interns to explore open problems in speech-to-speech translation: how to build models that let people talk naturally with anyone, in any language, in real time.

As an intern, you will own a focused research project on one of these questions, working closely with researchers and engineers on the team.

About Apple

Speech-to-speech translation is moving from cascaded pipelines to end-to-end models that listen, translate and speak in a single system.

Responsibilities

  • Many core questions remain open.
  • How do you translate simultaneously, before the speaker has finished?
  • How do you keep translated speech natural and expressive?
  • How do large multimodal models best combine speech understanding, translation and speech generation?
  • And how should these systems be evaluated?
  • You will have access to large-scale data and compute, and we encourage publishing results at top venues.

Requirements

  • Currently enrolled in a PhD or Master's program in Computer Science, Electrical Engineering, Linguistics or a related field.
  • Research experience in at least one of: speech recognition, speech synthesis, machine translation, NLP or multimodal learning.
  • Hands-on experience with deep learning toolkits such as PyTorch, JAX or TensorFlow.
  • Strong programming skills in Python.

Preferred

  • Research experience in speech-to-speech or speech-to-text translation, especially end-to-end, streaming or simultaneous approaches.
  • Research experience in text-to-speech or speech generation, such as neural TTS, speech tokenizers, audio codecs, or expressive and multilingual synthesis.
  • Experience training or fine-tuning large language models or multimodal (audio + text) models.
  • Familiarity with speech and translation evaluation, such as WER, BLEU/chrF/COMET-style metrics, MOS and human preference studies, and latency metrics.
  • Publications at relevant venues (e.g. Interspeech, ICASSP, ACL, EMNLP, NeurIPS, ICML, ICLR).
  • Excellent spoken and written communication skills.

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