Sökning: "unsupervised speech learning"

Visar resultat 1 - 5 av 13 uppsatser innehållade orden unsupervised speech learning.

  1. 1. Speaker Recognition using Biology-Inspired Feature Extraction

    Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

    Författare :Edvin Andersson; [2021]
    Nyckelord :Artificial Intelligence; AI; Machine Learning; Speaker Recognition; Technology and Engineering;

    Sammanfattning : Distinguishing between people's voices is something the human brain does naturally, using only frequencies picked up by the inner ear. The field of speaker recognition is concerned with making machines do the same thing using digitally sampled speech and data processing. LÄS MER

  3. 2. Instability of a bi-directional TiFGAN in unsupervised speech representation learning

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Matthaios Stylianidis; [2021]
    Nyckelord :;

    Sammanfattning : A major challenge in the application of machine learning in the speech domain is the unavailability of annotated data. Supervised machine learning techniques are highly dependent on the amount of labelled data and the quality of the labels. LÄS MER

  4. 3. Experiments in speaker diarization using speaker vectors

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Ming Cui; [2021]
    Nyckelord :Speaker Diarization; Embedding Extraction Module; Deep Learning; Supervised method; Unsupervised method; Talardiarisering; inbäddning av extraktionsmodul; djupinlärning; övervakad metod; oövervakad metod;

    Sammanfattning : Speaker Diarization is the task of determining ‘who spoke when?’ in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. It has emerged as an increasingly important and dedicated domain of speech research. LÄS MER

  5. 4. LjudMAP: A Visualization Tool for Exploring Audio Collections with Real-Time Concatenative Synthesis Capabilities

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Victor Hansjons Vegeborn; [2020]
    Nyckelord :;

    Sammanfattning : This thesis presents the software tool “LjudMAP," which fuses techniques of music informatics and unsupervised machine learning methods to assist in the exploration of audio collections. LjudMAP builds on concepts of the software tool, "Temporally Disassembled Audio," which was developed to enable fast browsing of recorded speech material. LÄS MER

  7. 5. Semi-supervised learning with HALFADO: two case studies

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Moustafa Aboushady; [2020]
    Nyckelord :;

    Sammanfattning : This thesis studies the HALFADO algorithm[1], a semi-supervised learning al- gorithm designed for detecting anomalies in complex information flows. This report assesses HALFADO’s performance in terms of detection capabilities (pre- cision and recall) and computational requirements. LÄS MER