DeepMyna Phase 2

DeepMyna 第二阶段

• 批准号: 10092974
• 金额: $ 139.07万
• 依托单位: HABITAT LEARN LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10092974
• 关键词: DeepMyna; Phase

Habitat Learn Limited (HLL) provides a personalised learning software, Messenger Pigeon, using its own Automated Speech Recognition("ASR") and generative AI to improve learning outcomes for students.HLL has 50,000+ UK and North American student-users, and 400 HEI (B2B2C with student as the end user) customers in UK and North America.DeepMyna builds upon IUK project DeepSpark, an AI toolkit optimising automated speech recognition (ASR) models for higher education, and DeepMyna Phase 1 Feasibility Study.ASR systems are widely used but can exhibit bias in a multitude of ways - one size does not fit all, leading to inequities, with certain types of speakers being transcribed incorrectly by models that otherwise perform well. Large-Language-Models ("LLM") can help speech recognition better predict words by rescoring the word output from spectrum data analytics, but can introduce domain-specific bias, causing misprediction or ignoring of key information.DeepMyna builds on a feasibility study completed in May 2023 which completed the following evaluation:* Bottlenecks identified which limit the adoption of trustworthy AI;* How DeepMyna's proposed evaluation and customisation model might support acceleration of adoption of trustworthy and responsible Artificial Intelligence (AI) and Machine Learning (ML) solutions;* SME use cases for accessing new markets and supporting their own product and market developmentAs part of the feasibility study Habitat Learn assembled a consortium comprising University of Southampton, Avanade Limited, Microlink PC (UK) Limited and Affiniti AI Limited.DeepMyna is proposed as a world-first bias-driven trustworthy automatic speech understanding (TASU) model evaluation and customisation platform with automated reporting and fine-tuning. Combining ASR and LLM to maximise the understanding of speech content, this regulatory platform will reinforce user-engagement, transparency, and privacy through bias-detection and user-feedback.DeepMyna will address identified bottlenecks:-* Lack of transparency in the diversity and representation of training data;* ASR models are?not explainable;* Lack of user input for fine-tuning; and* Lack of privacy protection.DeepMyna will be based on HLL's datasets compiled from 350,000 hours of lecture recordings with manually curated transcript and summary notes. HLL will extract provenance data for ASR training, such as language, background noise, gender, age, accent, etc, which will be applied to the evaluation matrix, to address the bias issues.

Habitat Learn Limited（HLL）提供个性化学习软件Messenger Pigeon，使用其自己的自动语音识别（“ASR”）和生成AI来提高学生的学习效果。HLL拥有50，000多名英国和北美学生用户，以及400名HEI用户。（B2B2C，学生作为最终用户）在英国和北美的客户。DeepMyna建立在IUK项目DeepSpark的基础上，一个优化高等教育自动语音识别（ASR）模型的AI工具包，以及DeepMyna第一阶段可行性研究。ASR系统被广泛使用，但可能会以多种方式表现出偏见-一种尺寸并不适合所有人，导致不公平，某些类型的说话者被其他表现良好的模型错误地转录。大语言模型（LLM）可以通过对频谱数据分析的单词输出进行重新评分来帮助语音识别更好地预测单词，但可能会引入特定领域的偏见，导致错误预测或忽略关键信息。DeepMyna基于2023年5月完成的可行性研究，该研究完成了以下评估：DeepMyna提出的评估和定制模型如何支持加速采用值得信赖和负责任的人工智能（AI）和机器学习（ML）解决方案;* 中小企业用于进入新市场并支持其自身产品和市场开发的使用案例作为可行性研究的一部分，Habitat Learn组建了一个由南安普顿大学、埃维诺有限公司、Microlink PC（英国）有限公司和Affiniti AI有限公司。DeepMyna被提议作为世界上第一个偏见驱动的可信自动语音理解（TASU）模型评估和定制平台，具有自动报告和微调功能。结合ASR和LLM，以最大限度地理解语音内容，这个监管平台将通过偏差检测和用户反馈加强用户参与，透明度和隐私。DeepMyna将解决已确定的瓶颈：-* 缺乏透明度的多样性和训练数据的代表性;* ASR模型是？无法解释的;* 缺乏用户输入进行微调;和 * 缺乏隐私保护。DeepMyna将基于HLL的数据集，这些数据集是从350，000小时的演讲录音中汇编而成的，并带有手动整理的成绩单和摘要笔记。HLL将提取用于ASR训练的来源数据，例如语言，背景噪音，性别，年龄，口音等，这些数据将应用于评估矩阵，以解决偏见问题。