I feel like this is a fairly common problem but I haven't yet found a suitable answer. I have many audio files of human speech that I would like to break on words, which can be done heuristically by looking at pauses in the waveform, but can anyone point me to a function/library in python that does this automatically?

An easier way to do this is using pydub module. recent addition of silent utilities does all the heavy lifting such as setting up silence threahold , setting up silence length. etc and simplifies code significantly as opposed to other methods mentioned.

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I had a audio file with spoken english letters from A to Z in the file "a-z.wav". A sub-directory splitAudio was created in the current working directory. Upon executing the demo code, the files were split onto 26 separate files with each audio file storing each syllable.

There are a lot of other cool features like word_alternatives_threshold to get other possibilities of words and word_confidence to get the confidence with which the system predicts the word. Set word_alternatives_threshold to between (0.1 and 0.01) to get a real idea.

I am having an issue with the text to speech option - the program automatically defaults to my laptop speakers. I have tried two wired and two Bluetooth headphones. Even though my computer settings were correct and directed the sound to the headset, when I attempted to do text-to-speech, it used the speaker option, bypassing the headphones. For every other program, the sound (music, text to speech, et al), it played through the headphones. I have also tried highlighting text and selecting play. I have the most recent Scrivener for Windows and am still using the trial version. I also have a Windows Surface Laptop 1. Help?

There are time when I have to go back into a page/slide and correct or adjust audio. Sometimes in the most random fashion, I make the change and then click update/save, when I then go to preview the page/slide or preview the scene and then get back to that page, the audio won't play at all. The audio is there. I can see it. I can even edit it if needed but when I try to play it, I can't hear it.

The only fix to this so far is that I have to create a completely new slide/page. Recreate all of the elements on that slide/page. Then add the audio from text to speech to the new page and sometimes that fixes it on the first try.

Thank so much for the reply. I am using the most recent update to Storyline 360. It has happened now on multiple projects and it appears to be a very random problem. I will repair the app and then report back on if that helps.

I'm having this same issue now with one slide. I tried recreating it (it is made from a screen recording, so I copied another slide that worked, then changed the start and end frame in the recording to include the part I wanted), then added audio notes and did text-to-speech and it doesn't play. Never had this happen before. Storyline 360 v3.50.24832.0

I'm still having this same issue. I've updated my software several times and done the app fix suggested. It is a very random issue. Sometimes I have no trouble and other times it won't stop happening. I am using SL360 v3.49.24347.0

I also had this issue. I tried repairing storyline via the above steps in a comment, tried rebuilding the slides a couple of times, and tried adding a space at the end of the text to speech text, but none of these worked for me. 

I finally just opened up the audio file to edit the actual audio, and selected a tiny section of silent audio, and deleted that tiny section. See screenshot attached.

rebuilding the slides a couple of times, and tried adding a space at the end of the text to speech text, but none of these worked for me. Growthtakeover

I finally just opened up the audio file to edit the actual audio, and selected a tiny section of silent audio, and deleted that tiny section. See screenshot attached.

Hi Margaret, I still encounter this issue far too often. It's very frustrating. I have tried every possible solution mentioned but nothing seems to work. Eventually, I still encounter the problem on a daily basis.

If that doesn't work, please open a case with our support team here to connect with our support engineers. This will allow us to request for logs from you that will help shed some light on what's happening.

The IEEE/ACM Transactions on Audio, Speech, and Language Processing is dedicated to innovative theory and methods for processing signals representing audio, speech and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems.

The Transactions encourages authors to make their publications reproducible by making all information needed to reproduce the presented results available online. This typically requires publishing the code and data used to produce the publication's figures and tables on a website; see the supplemental materials section of the information for authors. It gives other researchers easier access to the work, and facilitates fair comparisons.

It is now possible to submit for review and publish in Xplore supporting multimedia material such as speech samples, images, movies, matlab code etc. A multimedia graphical abstract can also be displayed along with the traditional text. More information is available under Multimedia Materials at the IEEE Author Center.

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in speech recognition and speaker verification tasks respectively. However, it is still an open challenging question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust speech recognition.

Speech applications in far-field real world settings often deal with signals that are corrupted by reverberation. The task of dereverberation constitutes an important step to improve the audible quality and to reduce the error rates in applications like automatic speech recognition (ASR). We propose a unified framework of speech dereverberation for improving the speech quality and the ASR performance using the approach of envelope-carrier decomposition provided by an autoregressive (AR) model.

Question answering requiring numerical reasoning, which generally involves symbolic operations such as sorting, counting, and addition, is a challenging task. To address such a problem, existing mixture-of-experts (MoE)-based methods design several specific answer predictors to handle different types of questions and achieve promising performance. However, they ignore the modeling and exploitation of fine-grained reasoning-related operations to support numerical reasoning, encountering the inadequacy in reasoning capability and interpretability.

The speaker recognition evaluation is conducted in a framework in which three score distributions and two decision thresholds are employed, and the statistic of interest is an average of the two weighted sums of the probabilities of type I and type II errors at the two thresholds correspondingly. And data dependence caused by multiple use of the same subjects exists ubiquitously in order to generate more samples because of limited resources.

In the second episode of the Speak Ola Podcast, host Tony Wilson discusses the famous "pay the price" speech given by Australian Football Hall of Fame inducted coach, Alan Jeans, during halftime in the 1989 Grand Final. Jeans, who had a successful coaching career spanning 31 years, was known for his charismatic and powerful speeches. The "pay the price" speech, which used the metaphor of a boy buying a pair of shoes, was remembered by many players for its motivational impact. Despite the physical challenges the team faced during the game, Jeans' speech helped them regain their focus and determination. The episode also includes reflections from players who worked with Jeans, highlighting his influence and the respect he commanded.

Hello, that is me, Tony Wilson, and welcome to the second episode of the Speakola podcast. Here in Australia, at least some of the restrictions relating to Covid 19 are easing somewhat, and we're about to have a return to AFL football on June the 11th. So I thought to whet the appetite, I might feature a famous AFL speech this week. It's Allan Jeans and his Pay the Price speech, which relates to a pair of shoes, and he gave the speech at halftime in the 1989 Grand Final. And it's a speech that's very dear to my heart in the sense that I've just spent six months writing about the 1989 Grand final for a book called '1989: the Great Grand Final'. To give you some context on Allan Jeans. He's an Australian football Hall of Fame inducted coach who lived between 1933 and 2011.

He actually became a coach at a very young age of just 27, and that was at the St Kilda Football Club where he spent 16 years and he had immediate success in his first season of 1961. He took the Saints to their first final series since 1939, and then just a few years later in 1965, he takes them to a grand final, their first since 1913. And in '66, I mean, that's just a very famous day in St Kilda history. The only day where the Saints got home and won a VFL or AFL premiership.

In 1980, he was appointed at Hawthorn. He'd been out of the game for a couple of years at that point and replaced David Parkin. He was not favoured to get the job -- it looked like Peter Hudson was going to get it, but apparently Jeans was only paid, less than $20,000. And one of the great investments because he went on a winning spree.

The Hawks made the finals in 1982 and then won the the premiership in '83 and played in Grand Finals from 1983 to 1989, seven consecutive grand finals of which they won four and lost three. Jeans himself missed out on the 1988 premiership because he suffered a brain aneurysm at the end of 1987. 152ee80cbc

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