Alexandros Lazaridis

As a product owner Alexandros Lazaridis has led 2 teams in the Data Analytics & AI (DNA) group, over the last 3 years. Working mainly on 3 products:

- the Swisscom Voice Assistant on Swisscom TV Box

- the VoiceBot & Chatbox for automating the customer interaction with Swisscom,

- the Conversational Insights for understanding and analyzing what the customer needs.

Focusing on speech processing topics (e.g. speech recognition, voice activity detection, emotion recognition, etc.) , on natural language processing topics (e.g. topic/intent classification, retrieval system (FAQ), machine translation, etc.) and on large language models (LLMs) using openAI and on-premise tools/models

• Managed backlog prioritization, ensuring alignment with business goals and stakeholder expectations.
• Collaborated with engineering teams on technical feasibility assessments, resulting in realistic scope definition for projects.
• Developed comprehensive roadmaps outlining the product vision, goals, and milestones for successful execution.
• Proactively identified risks associated with project timelines or deliverables, implementing mitigation strategies when necessary.
• Collaborated with cross-functional teams to develop high-quality products that met customer needs.
• Analyzed customer feedback to identify areas for improvement.
• Oversaw entire product deployments from vision and creation to rollout and delivery.
• Mentored junior team members in Agile methodologies and best practices for effective Product Owner role execution.

• Main focus of the Speech Processing R&D Group:

- Automatic Speech Recognition (ASR) on Swiss Languages and Dialects,

- Language Identification (LID),

- Speaker Identification/Verification (SI/SV),

- Emotion Identification & Sentiment Analysis (SER),

- Text to Speech Synthesis (TTS),

- Data Selection and Active Learning,

- Natural Language Processing & Understanding (NLP/NLU),

- Neural Machine Translation (NMT),

- Swiss German Dialects Text Normalization

• Leading R&D Projects:

- Swiss German Automatic Speech Recognition (in collaboration with Microsoft Research Center in Munich)

- DAHL: Domain Adaptation via Hierarchical Lexicons (In collaboration with Idiap Research Institute and École Polytechnique Fédérale de Lausanne (EPFL))

- Building A High German to Swiss German Phonetic Dictionary (In collaboration with the University of Zurich (UZH))

• Supervising and co-Supervising Master Thesis and PhD Projects

- In collaboration with Idiap Research Institute and École Polytechnique Fédérale de Lausanne (EPFL) - End-to-end Speaker Recognition from Speech, Self-attention for speech emotion recognition, End-to-end Language Recognition from Speech using Bottleneck Features, Data Selection for Speech Recognition Training, Domain Adaptation in End-to-End Speech Recognition, Key-word Search/Spotting using Convolutional Neural Networks, Semi-Supervised Data Acquisition and Explorations

- In collaboration with La Haute école spécialisée de Suisse occidentale de Fribourg (HEFR) - Swiss German Normalization with Neural Networks, Swiss German dialects Text Normalization using Neural Network Machine Translation-based Approaches and Word Embeddings

- In collaboration with University of Zurich - Fairness in AI.

• Research Associate in speech processing working on several projects: on speech synthesis and prosody modelling on the SIWIS (Swiss NSF) project, on multilingual automatic speech recognition on the SUMMA (EU-H2020) project and on automatic speech recognition for Swiss German dialects on Swisscom project.

• Post-doctoral Researcher in speech processing
• Supervised by Dr
• Philip N
• Garner
• Working on SIWIS Swiss NSF project
• The SIWIS (Spoken Interaction With Interpretation in Switzerland) project aims to investigate spoken language translation, where translation carries not only spoken content, but also prosody/emphasis in speech
• In the framework of SIWIS, worked on prosody modelling (phone duration and intonation modelling), regional accent identification using acoustic and prosodic features, stress and accent modelling, and statistical parametric speech synthesis using hidden Markov model (HMM) and deep neural network (DNN) techniques in English, German and French languages
• Research interests include signal processing, prosody modelling, speech synthesis, emotional/affective speech processing, data mining & machine learning, voice conversion.

• Course: Digital Design I.

• Courses: Digital Signal Processing, Informatics I and II.

• Continuing working on prosody modelling of neutral and emotional speech
• In parallel working on neutral and emotional speech synthesis using statistical parametric speech synthesis approaches.

• Course: Speech Technology.

• Course: Introduction to Computers.