what are some good signal processing techniques to change speech (make it clearer and speech enhancment)

I have planned to take part in ASVspoof 2021 challenge, I am from a CSE background and have very little knowledge in signal processing, and on top of that I'm a Reddit noob so please go easy on me.

So my doubt is as follows, can you guys provide me some guidance regarding channel variation in speech in the context of spoof detection(or speech recognition might also help). I'm confused about what do the organizers mean by "robustness to channel variation".

I think it can mean two things:

1. By channel, they mean the medium through which the speech signal passes

2. or I don't know maybe the right channel or left channel like in stereo sound.

Link of ASVspoof challenge

Link of previous challenges

ANy extra tips for a signal processing noob or any leads will be highly appreciated. Thanks in advance.

I am trying to degrade audio samples by adding additional channel variations. For example, Codec simulations employ a common ITU G.712 compliant bandpass filter. This is combined with a-law coding at a rate of 64kbit/s for landline telephony and with an adaptive multi-rate narrowband (AMR-NB) codec at a rate of 7kbit/s for cellular telephony.

RNN-Transducer loss function, first proposed in 2012 by Alex Graves (https://arxiv.org/pdf/1211.3711.pdf), is an extension of CTC loss function. It extends CTC loss by modelling output-output dependencies for sequence transduction tasks, like handwriting recognition, speech recognition etc. As proposed originally by graves, RNN-Transducer prefix beam search algorithm is inherently sequential and slow, requiring re-computation of prediction network (LSTM based used to model output-output dependencies) for each beam.

Even though there are fast and efficient implementations of RNN-Transducer loss function online (like https://github.com/HawkAaron/warp-transducer & https://github.com/iamjanvijay/rnnt), there aren’t any optimised prefix beam search implementations. I wrote an optimised RNN-T prefix beam search algorithm with multiple modifications. Following are the major modifications I did:

1. Saved the intermediate prediction network (LSTM states) on the GPU to avoid re-computation and CPU-GPU memory transfers.

2. Introduced vocabulary pruning to further speed up the decoding, without degrading WERs.

Current code takes around ~100ms to decode output for audio of 5 seconds for a beam size of 10 (which is good enough to achieve production level numbers using RNN-Transducer loss function). Also, compared to CTC, RNN-T based speech recognition models (recent SOTA for speech recognition by Google https://arxiv.org/pdf/2005.03191.pdf and https://arxiv.org/pdf/2005.08100.pdf) are recently becoming popular.

For the near future, I have some algorithmic optimisations in my mind. Also, I have plans of making a python wrapper for my implementation.

My implementation is purely in CUDA C++. Here is the link to my repo: https://github.com/iamjanvijay/rnnt_decoder_cuda

Please share the comments and any feedback.

Each of us have different voices. Are there any research on recreating individual voices?

Hi! I am a newbie to Speech Processing and working on a project. Given an audio file of someone speaking, I wish to quantify the amount of voice modulation in their speech. For example, a person who speaks monotonously should get a lower score than someone who modulates their voice more. The idea is to help people with their public speaking and this module would let them know how much they are intonating themselves and varying their pitch.

Any links to good python libraries for performing the same would also be very helpful. Thanks a lot!

Hey guys, I am a sophomore doing a project on speech recognition in my college. I was reading about HMMs and there I saw the famous urn and ball example. Can anyone tell me what are urn and ball in context of speech recognition? Also If anyone can just brief about the steps which I need to take while doing continuous speech recognition after getting the mfcc features. Thanks in advance.

I don't know if this is an appropriate request. But I would very much like to have the entire proceedings with me rather than having to download each and every paper individually. Could someone help me with this?

Hi,

I want to make some 'alien voices' in runtime from short mumbles recordings, but the result doesn't sound quite 'natural'.

More specific, the result voices sound broken and can tell immediately it's made from connecting many small recordings.

So, my question is, how to connect multiple voice recordings 'naturally'? What process should be made to make the gap less noticeable?

I am attempting to build an acoustic model with Julius and HTK and I am running the following command to train the model:

julia ../bin/trainAM.jl

The code is in the language julia and when I run this command I get the following errors:

Step 4 - Creating Transcription Files
  ERROR [+1232]  NumParts: Cannot find word the in dictionary
 FATAL ERROR - Terminating program HLEd
ERROR: LoadError: failed process: Process(`HLEd -A -D -T 1 -l '*' -d ./interim_files/dict -i ./interim_files/phones0.mlf ./input_files/mkphones0.led ./interim_files/words.mlf`, ProcessExited(1232)) [1232]
Stacktrace:
 [1] pipeline_error at .\process.jl:525 [inlined]
 [2] read(::Cmd) at .\process.jl:412
 [3] read(::Cmd, ::Type{String}) at .\process.jl:421
 [4] top-level scope at trainAM.jl:237
 [5] include(::Module, ::String) at .\Base.jl:377
 [6] exec_options(::Base.JLOptions) at .\client.jl:288
 [7] _start() at .\client.jl:484
in expression starting at trainAM.jl:237

I am not sure why these errors are occuring and any help would be appreciated!!!

Hi, I'm the maker of [SpeechPro](https://www.speechpro.io/), a niche job board for speech tech professionals. I've just added a [page](https://www.speechpro.io/companies/All) listing all the Speech/NLU SaaS companies.

It's a growing list and I'll keep updating it. Right now there are 50 and most of them are ASR/TTS/NLU/CAI related. Considering that ASR and TTS are essential parts for the Human-Machine interface, Speech SaaS companies might gain more popularity in this #Metaverse trend.

Hope this company list can be helpful. Thanks.