The first thing you need to understand if your accuracy just lower than expected or very low. If it's very low most likely you misconfigured the decoder. If it's lower than expected, you can apply various ways to improve it.
The first thing you should do is to collect a database of test samples and measure the recognition accuracy. You need to dump utterances into wav files, write reference text and use decoder to decode it. Then calculate WER using the word_align.pl tool from Sphinxtrain. Test database size depends on the accuracy but usually it's enough to have 30 minutes of transcribed audio to test recognizer accuracy reliably.
Only if you have a test database you can proceed with recognition accuracy optimization.
The top reasons of the bad accuracy are:
sox --i /path/to/audio/file
There are multiple levels to fight with noise and corruption of the audio. Noise cancellation algorithm modify the audio itself, feature denoising can cleanup features. You can also reduce noise and mismatch in model level, just by adapting the model.
Recent CMUSphinx code has noise cancellation featur. In sphinxbase/pocketsphinx/sphinxtrain it's 'remove_noise' option. In sphinx4 it's Denoise frontend component. So if you are using latest version you should be robust to noise in some degree already. Most modern models are trained with noise cancellation already, if you have your own model you need to retrain it.
The algorithm impelmented is spectral subtraction in on mel filterbank. There are more advanced algorithms for sure, if needed you can extend the current implementation.
It's not recommended to perform an external noise suppression because many noise cancellation algorithms corrupts speech spectrum in unusual ways and reduce speech recognition accuracy even more than the noise itself. For that reason you need to be very careful when selecting the noise cancellation algorithm. Only some of them like Ephraim Malach or Kalman will work properly.
A reasonable way to fight with noise is to adapt the model or train it on a noisy audio. MLLR adaptation usually compensates quite significant part of the noise corruption. It's so-called multistyle training.
As for now pocketsphinx does not support confidence scores and out-of-grammar words detection in grammars. We are working on the implementation but it is not ready yet. We support the following modes that can help you to recognize in continuous stream:
Keyword spotting mode You can configure a list of keyphrases to search for and specify the detection threshold for each of them. This mode reliably works in continuous speech stream and can be used for keyword activation. The relevant options of pocketsphinx are -kws and -keyphrase. The methods are ps_set_keyphrase and ps_set_kws.
Large vocabulary decoding mode with a language model. In case of large vocabulary decoding you can retrieve result confidence scores, they should be more or less relable.
If you want to recognize several commands, you can use keyword spotting mode or keyword activation mode combined with the switch to grammar to perform actual operation.
Sphinx4 has support for skipping out-of-grammar phrases. In default configuration it returns '<unk>' word if something is not matched with the grammar. This feature can not be tuned yet and there is no confidence in grammar mode.
Keyword spotting mode is not implemented in sphinx4.
In large vocabulary decoding mode sphinx4 should return proper confidence for recognition result.
If continuous is showing READY and doesn't react to your speech it means that pocketsphinx recording silence. The reasons for that are:
''Warning: Could not find Mic element''
(try to change device with
CMUSphinx itself is language-independent, you can recognize any language. However, it requires an acoustic model and a language model. We provide prebuilt language models for many languages (Enlish, Chinese, French, Spanish, German, Russian, etc) in download section.
The process of building a new language model consists of the following steps:
Most steps are described in CMUSphinx Tutorial For Developers
When you report about problem always provide the following information:
If you want to get fast answer, submit also the following information
See How to ask questions howto for more details
CMUSphinx uses mel-cepstrum MFCC features with noise tracking and spectral subtraction for noise reduction. There are various types of MFCC which differ by number of parameters, but not really different for accuracy (it might be a few percent worse or better).
The interpretation of MFCC (Roughtly introduced Alan V. Oppenheim and Ronald W. Schafer. From Frequency to Quefrency: A History of the Cepstrum. IEEE SIGNAL PROCESSING MAGAZINE) is not applicable as such, and the use of 12 or 13 coefficients seem to be due to historical reasons in many of the reported cases. The choice of the number of MFCCs to include in an ASR system is largely empirical. To understand why any specific number of cepstral coefficients is used, you could do worse than look at very early (pre-HMM) papers. When using DTW using Euclidean or even Mahalanobis distances, it quickly became apparent that the very high cepstral coefficients were not helpful for recognition, and to a lesser extent, neither were the very low ones. The most common solution was to “lifter” the MFCCs - i.e. apply a weighting function to them to emphasise the mid-range coefficients. These liftering functions were “optimised” by a number of researchers, but they almost always ended up being close to zero by the time you got to the 12th coefficient.
In practice, the optimal number of coefficients depends on the quantity of training data, the details of the training algorithm (in particular how well the PDFs can be modelled as the dimensionality of the feature space increases), the number of Gaussian mixtures in the HMMs, the speaker and background noise characteristics, and sometimes the available computing resources.
In semicontinuous models CMUSphinx uses specific packing of derivatives to optimize vector quantization and thus compress model better. Through years various features were used. Mostly they were selected by experiment.
Spectral subtraction of noise is one thing which differs CMUSphinx MFCC from other popular MFCC implementations, it is a simple extension that provides robustness to noise because it tracks and subtracts stable noise component in mel filter energy domain.
CMUSphinx implements keyphrase spotting mode in pocketsphinx decoder. To recognize a single keyphrase you can run decoder in “keyphrase search” mode.
From command line try:
pocketsphinx_continuous -infile file.wav -keyphrase “oh mighty computer” -kws_threshold 1e-20
From the code:
ps_set_keyphrase(ps, "keyphrase_search", "oh mighty computer"); ps_set_search(ps, "keyphrase_search); ps_start_utt(); /* process data */
You can also find examples for Python and Android/Java in our sources.
Threshold must be tuned for every keyphrase on a test data to get the right balance missed detections and false alarms. You can try values like 1e-5 to 1e-50.
For the best accuracy it is better to have keyphrase with 3-4 syllables. Too short phrases are easily confused.
You can also search for multiple keyphrase, create a file keyphrase.list like this:
oh mighty computer /1e-40/ hello world /1e-30/ other_phrase /other_phrase_threshold/
And use it in decoder with -kws configuration option.
pocketsphinx_continuous -inmic yes -kws keyphrase_list
This feature is not yet implemented in sphinx4 decoder.
Pronunciation evaluation is NOT supported by CMUSphinx, it is a whole big area of research which requires new algorithms and new data. The statistical hypothesis testing apparatus must be applied to distinguish match hypothesis and mismatch hypothesis. Both hypothesis should be evaluated against proper data. Thus you need to train models with accented and non-accented speech and you need to collect enough data for both. You also need a scoring algorithm to evaluate both hypothesis.
Though CMUSphinx can not be used here as is, you still can get a lot of features by using CMUSphinx for pronunciation evaluation like audio processing, scoring, dictionary access or model input-output.
The research on computer-aided language learning is pretty wide. The good starting point for it is the following paper, though you will have to read some more papers to get the full picture.
The stack trace is usually the following:
INFO: fe_interface.c(289): You are using internal mechanism to generate the seed. INFO: feat.c(289): Initializing feature stream to type: '1s_c_d_dd', ceplen=13,CMN='current', VARNORM='no', AGC='none' INFO: cmn.c(142): mean= 12.00, mean[1..12]= 0.0 INFO: acmod.c(153): Reading linear feature trasformation from acoustic/feature.transform INFO: mdef.c(520): Reading model definition: acoustic/mdef INFO: bin_mdef.c(173): Allocation 104810 * 8 bytes (818 KiB) for CD tree INFO: tmat.c(205): Reading HMM transition probability matrices: acoustic/transition_matrices (After it I have crash) Stack trace: ntdll.dll!774f8db9() [Frames below may be incorrect and/or missing, no symbols loaded for ntdll.dll] ntdll.dll!774f8cc8() > msvcr100.dll!_lock_file(_iobuf * pf) Line 236 + 0xa bytes C msvcr100.dll!fgets(char * string, int count, _iobuf * str) Line 71 + 0x6 bytes C sphinxbase.dll!002319ef() msvcr100.dll!_unlock(int locknum) Line 375 C msvcr100.dll!_unlock_file(_iobuf * pf) Line 313 + 0xe bytes C msvcr100.dll!fread_s(void * buffer, unsigned int bufferSize, unsigned int elementSize, unsigned int count, _iobuf * stream) Line 113 + 0x8 bytes C msvcr100.dll!fread(void * buffer, unsigned int elementSize, unsigned int count, _iobuf * stream) Line 303 + 0x13 bytes C sphinxbase.dll!00231743() sphinxbase.dll!00231cbf()
sphinxbase was compiled iwth MultiThreadedDLL runtime, see in vcxproj
If you don't compile your project with similar setting it will crash. Use proper runtime library or recompile sphinxbase
Device file /dev/dsp is missing because OSS support is not enabled in the kernel. You can either compile pocketsphinx with ALSA support by installing alsa development headers from a package libasound2 or alsa-devel and recompiling or you can install oss-compat package to enable OSS support.
The installation process is not an issue if you understand the complexity of audio subsystems in Linux. The audio subsystem is complex unfortunately, but once you get it things will be easier. Historically, audio subsystem is pretty fragmented. It includes the following major frameworks:
The recommended audio framework on Ubuntu is pulseaudio.
Sphinxbase and pocketsphinx support all the frameworks and automatically selects the one you need in compile time. The highest priority is in pulseaudio framework. Before you install sphinxbase you need to decide which framework to use. You need to setup the development part of the corresponding framework after that.
For example, it’s recommended to install libpulse-dev package to provide access to pulseaudio and after that sphinxbase will automatically work with Pulseaudio. Once you work with pulseaudio you do not need other frameworks. On embedded device try to configure alsa.
Unfortunately we don't provide universal models for different bandwidths (8khz models are 10% worse in accuracy) and we can not detect sample rate yet. So before using decoder you need to make sure that both sample rate of the decoder matches the sample rate of the input audio and the bandwidth of the audio matches the bandwidth that was used to train the model. A mismatch results in very bad accuracy.
First of all you need to understand the difference between sample rate and frequency bandwidth. Sample rate - the rate of samples in the recording. Sample rate 16000 means that there are 16000 samples collected every second. You can resample audio with sox or with ffmpeg:
sox file.wav -r 16000 file-16000.wav ffmpeg file.mp3 -ar 16000 file-16000.wav
Then there is a bandwidth - the range of frequencies included in the audio, it doesn't change with sample rate. If audio had frequencies up to 8khz only no matter how you resample the bandwidth will be still the same. And due to mismatched bandwidth the audio will not be recognized properly.
To check the bandwidth of the audio you need to see it's spectrum in audio editor like Wavesurfer. You'll see dark spectrum only up to 4khz if audio is 8khz. If your audio was recorded from telephone source or was compressed by voip codec most likely it has only 8khz bandwidth.
So first you need to do the following: make sure that frontend sample rate matches the sample rate of the audio. And then make sure that bandwidth of the audio matches your model bandwidth. Use en-us generic model for 16khz bandwidth and en-us-8khz generic model for 8khz bandwidth.
CMUSphinx decoders do not include format converters, they have no idea how to decode encoded audio. Before processing audio must be converted to PCM format. Recommended format is
16khz 16bit little-endian mono. If you are decoding telephone quality audio you can also decode
8khz 16bit little-endian mono, but you usually need to reconfigure the decoder to input 8khz audio. For example, pocketsphinx has
-samprate 8000 option in configuration.
You can find out file format using
soxi command or
You can convert your encoded audio into required format with ffmpeg or with other specialized decoder:
ffmpeg -i file.mp3 -ar 16000 -ac 1 file.wav
If you are writing software you might want to integrate the format converter like ffmpeg/avconv as a separate library.
Sound recording is pretty unique in various software platforms. For example, Android uses Java Audio Recorder. Python bindings use PyAudio. On Windows we record with WinMM API, on Windows Phones there is another API. All those APIs are not compatible with each other.
For that reason there is no standard audio recording framework in pocketsphinx/sphinxbase. You have to record audio with the platform API and just pass the data into decoder. For example, if you want to record audio in Android NDK, you can use OpenSL ES library or any other framework.
There is also libad library which works on Linux/Windows/Mac, but it is very limited and we are not going to extend it into other platforms.
No, you can't. The CPU is too slow for large vocabulary speech recognition. Phone CPU is usually 9 times slower than desktop. Speech recognition with unlimited vocabulary requires very big computational and memory resources (terabytes of memory) and thus it's very hard to do that in iPhone on other embedded device. iPad is easier.
Google has put very big effort to make their engine work offline for dictation, and still it prefers to send data to the server because it is significantly more accurate.
Because of that most of the solutions running on small devices use limited vocabulary. Though this vocabulary can be large enough so you will not notice that. Usually 500-1000 words is enough to cover most practical situations.
To train a language model you need texts from your domain (words and expressions). Language model training is described in tutorial.