Speech synthesis based on a limited speech

corpusRudy Marsman | VU University | NISV

Netherlands Institute for Sound and Vision (NISV) | Beeld & Geluid

Beeld en Geluid

• collects, preserves and opens the Dutch audiovisual heritage for as many users as possible• one of the largest audiovisual archives in Europe. The

institute manages over 70 percent of the Dutch audiovisual heritage• Was interested in ways to re-use old Polygoonjournaals

footage• Text-To-Speech engine based on Philip Bloemendal

Philip Bloemendal

• Famous anchorman• Iconic voice• https://www.youtube.com/watch?v=31tClHJ2tfQ

Research

• Can the current corpus of audio recordings of Bloemendal be used to construct a TTS engine?• How large percentage of the Dutch language can be constructed

with the current corpus?• What can we do to improve?• How well is the text-to-speech engine recognizable as Philip

Bloemendal?• How well comprehensive are the constructed audiofiles?

How large percentage of the Dutch language can be constructed with the current corpus?

• Constructing the corpus• How many ‘Polygoonjournaals’ • Openbeelden – OAI (Open Archives Initiative)• Extract audio• Speech analysis – roughly 35000 distinct words • XML files

• Evaluation• Metrics• Corpora• Language changes

How large percentage of the Dutch language can be constructed with the current corpus?

• Approach: 4 corpora to test against• Contemporary news articles (same domain, different time) | 50

articles• News articles from the 1970s (same domain, time) | 50 articles• E-books (different domain, various times) |6 books• Tweets (different domain, different time) | 1000 tweets

• Evaluation• Number of distinct words• Number of sentences

What can we do to improve performance?

• It is to be expected that many (contemporary) words have not been pronounced by Philip• Various approaches

• Change format (Lowercase, diareses)• Numbers• Finding synonyms• Decompounding

Finding Synonyms

• Open Dutch Wordnet: Dutch lexical semantic database• Maarten Postma et al.• Yields synsets (e.g. Hoofdmeester -> Rector, Schoolhoofd)• Computationally expensive

Decompounding

• Dutch language allows for compounding words• School, hoofd -> Schoolhoofd• Regen, water -> regenwater• Staat, hoofd -> StaatShoofd

• Each word is distinct in the corpus• Decompounding is computationally expensive• Computationally expensive for large corpora, long words• Constructed Bigrams and Trigrams

Results (words)

Dataset Unique words

Unique words found

After synsets After decompounding

Contemporary news

2743 2019 2106 2448

Old news 16191 7703 8261 11541Tweets 27180 7692 8446 13440Books 26575 11440 12922 20207

Results (sentences)

Dataset Unique sentences

Unique sentences found

After synsets After decompounding

Contemporary news

1022 106 110 186

Old news 2626 183 190 301Tweets 8937 174 181 296Books 56106 9387 11385 18271

How comprehensible / recognizable are sentences• 8 people tested the software• Philip was recognized (or ‘that news guy’)• Words with more consonants were easier to recognize• When user input their own sentences, more recognition• When sentences were demonstrated without subtitles, less• Speed of software / GUI limited testing capabilities

The use of Deep Neural Networks in colorizing

videoRudy Marsman | VU University | NISV

Neural Networks

• Recent progress in computational power made implementation of Deep Neural Nets possible• Neural Net trained on large training set can accurately

make predictions in real-world examples

Zhang et al.

• Richard Zhang et al. trained a neural net to colorize images• Trained on over a million images• Fools humans into thinking colorized photo is original 20%

of time• Resizes image to fit input layer of 200x200 pixels• Gained popularity in news website / forums

Zhang et al.

Implementation on video

• Extract individual frames from video using FFMPEG• Colorize each individual frame• Re-compile video and attach original audio file

Example

• https://www.youtube.com/watch?v=olsO2rOy_i4

Applications

• Colorized videos are more ‘tangible’ and ‘alive’ than black/white• Showing colorized Polygoonjournaals can augment TTS

engine• General positive responses on technology may increase

attention to NISV collection• NISV Employees were enthousiastic

Issues

• Each frame is considered independent and is colorized thusly• Artifacts appear between frames• Slow performance without use of Nvidia GPU• Low resolution• Predicted colors still far from perfect

Conclusions

• Current corpus covers many of often used words• Various implemented approacheds increase coverage• Low coverage for sentences -> real world approach may

need improvement• Audio is recognizable and understandable• Neural Networks may be used to colorize video footage

Discussion