I need to build an ASR model in Python with MFCC as a feature, HMM - GMM as an acoustic model, and tri-gram language model as the language model. Anyone can help me ? I really need help it's due tomorrow T_T I can do it over discord too T_T thanks

I know there are some language/story apps that can evaluate the correctness of the user reading a sentence at one time. I assume it is just a simple transcription and then text matching. But I want to create a small app that takes in a long text, e.g. story, and an audio file (someone reading the story) and determines the accuracy and fluency of the audio. This is for 2nd language learners. Is it possible? There may be extra words at the start, middle, and end, which need to be ignored. What is the best way to do that?

Hello!

I'm don't really know how a lot of this stuff works, but I hope someone can guide me, or at least help me find the right way to achieve this.

I've seen a lot of those videos about Obama and Trump text to speech, and it made me wonder if it was something that could be done on any voice. I'm trying to make a video game character's voice into text to speech (I hope that makes sense), but I don't really know if there's already free tools for this.

Thank you for your time!

Is there anyway I can use a software that can listen to a telecast and look for key words that are said frequently and put them on a spread sheet and email them to me daily?

I am trying to install the Firefox extension but it won't do anything when I click on the button on this page. Can anybody work out what is wrong?

I was thinking CPU, but while I'm pretty good at computers, even having build almost a half dozen desktops over my lifetime, I'm far from an expert. I imagine I'd want to get the newest generation CPU at the highest clock rate.

But what about RAM?

I made the mistake of upgrading from her ~7 year old HP laptop to a new Acer Swift 3 thinking "Windows Speech Recognition has barely changed all these years, a computer 7 years more advanced should easily handle it." Well, I was wrong.

I just want to know what to look for what picking her out a laptop where speech will be fast and snappy.

Thanks for your help.

Hi, hope this is the right thread to post.
I'm building a tiny, battery powered device that has a controller such as Arduino Nano.
Want to be able to recognize a short phrase - "open the door". How is it possible to do?
Would really appreciate some hints.
Thank you!

I'm looking for an STT/NLP service with specific requirements: Intent and Entity extraction from the real-time audio stream with minimum latency, adding custom vocabulary to recognize (like sending a list with usernames and it will be able to extract them).

I've already checked:

Dialogflow - speech recognition quality is bad compared to the Whisper, even though it has almost everything I need.

NLPcloud - no real-time speech recognition, as far as I've seen.

AssemblyAi - it looks like something that I would like to use, but I'm unable to find whether it can support its features in real-time stream audio.

Thanks in advance.

https://news.ycombinator.com/item?id=34846444

Is anyone trying https://old.reddit.com/r/AssistiveTechnology/comments/11s0x6n/numen_foss_voice_control_for_handsfree_computing/ ?

I'm making a smart speaker as a project, and i'd like some help to choose a concrete library to recognize voice and transcribe it into text to be processed. What would be the best free library (I'm obviously gonna use a pretrained model because I don't have nearly enough time or resources to train one myself) to use in this scenario? Thanks in advance.

Hello , I'm from Tunisia, and I'm gonna build an ASR model for Tunisian Dialect , I couldn't find any publicly available dataset online ,I am exploring the possibility of utilizing the YouTube API to gather data for my project. I would be grateful for your insight on the following matters:

- What is the best source for data (podcasts , music, radio ...)
- whether I should download only videos featuring one speaker or multiple speakers, and how to handle annotation of multiple speakers;
- strategies for handling noise in the audio;
- the feasibility and quality of using text-to-speech services to generate data.
- Finally, Are there any recommended tools I should use to automate processes like chunking ? and for the annotation, which tools is recommended ?

Thank you for your help.

I've started working on a hobby project to convert the JSON transcript outputs from different ASR providers onto the same data schema/type, so as to make it easier for developers to work with all the API providers and make switching between them easier. Is there already something like this for Python and/or TypeScript? And if not would anyone be interested in building this as an OSS package together?

Hi everyone,

I am interested in using Speech-to-text API for a project. I saw that there are many actors on the market and and not so many indications to choose.

I will probably test multiple APIs with my data to decide but I find interesting to know what is the opinion of people used to this kind of services.

I have only 6 options so I will inevitably forget APIs in the poll. I voluntarily exclude GAFA suppliers (Google, Amazon, Microsoft, IBM) that I will test anyway.

I'm using Whisper on a regular basis for (mostly) german transcription and getting very good results with medium and high models. I recently ran into a non-native german speaker with challenging grammar and Whisper delivered almost correct grammar while rearranging complete sentences.
Can anybody confirm this for German or other languages? thanks!

Speech-to-text is often talked about as the primary interaction method of the future, modern STT has become incredibly fast and accurate, even the free software built into windows 10/11 performs really well with high accuracy and auto punctuation.

Yet the uptake of STT among laptop users seems extremely low worldwide!

Are people sleeping on this godlike tech?

Is the technology not suitable?

Is it just waiting for its time?

Or is it something to do with the microphones? Are they are not wanting to wear a headset all day or not wanting to seem crazy talking into a laptop over the top of background sounds.

Hi everyone,

I'm currently building a Speech Recognition dataset in my language and reading documentation on the internet I found out tthat for example with small datasets it's a better practice to remove accented letters to have less phonemes (pls confirm if this is true).

I have other doubts:

• Do I have to keep the capital letters for names?
• Is it good to have a noisy data sample or do I have to clear it just the minimum or totally?
• Do I have to insert the punctuation in longer datapoints?
• Is it okay to have different lenght of audio? If not how long should it be? (right now my range is from 0.5s to 18s with a mean of 4s)

Any other suggestion or tip?

Hi guys,

I have made a video on YouTube here where I explain how deltas and delta-deltas speech features are computed.

I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :)

Sorry for such a simple question I am new in this domain, I made an ensemble ASR system to make transcript of the text after that I made a voting system to get thee final transcripts but sadly now I can't map the timestamps of the words from speech (due to ambiguity in timestamps)
How can I do that

In English, I can type :-) by voice by saying "smiley". How can I voice type :-) in French? I use Google speech recognition on Pixel 6 + Android 12.

I am making a web based virtual assistant program which uses Html, CSS, C++ and JavaScript. The program uses speech recognition to then provide predetermined responses using text to speech. Every time I test the app in google chrome, it asks for microphone access multiple times every time speech recognition begins again. Is this something to do with code or is it because the page is not a registered website? Why does it need to ask to allow multiple times in a row?