Russian voice to text online service.

Hi,

We added support of a new language - Russian. So, you can process any of your recordings in Russian now. Just log on SpokenData, submit your data and get automatic text transcript for free in few minutes. Another option is to provide us with URL of YouTube, Vimeo or other on-line services where your data is. We download the data and convert them into text quickly. You are notified by email when the conversion of audio into text is done. You can also edit the transcript yourself in our web editor later.
If you are a developer, feel free to integrate our API. It's easy.

You do not have SpokenData.com account yet? Just register here and you can process your data in 1 minute!

American Spanish speech to text for free!

Hello,

We are happy to announce that we support American Spanish now. So, if you have any voice recordings, you can process them in SpokenData to get automatic text transcript for free now. As our service is in cloud, it is very easy for you to get the text. Just take you audio or video files in Spanish and upload them.
The second option is to provide us with URL of YouTube, Vimeo or other on-line services. We download the data and convert them into text quickly. You are norified by email when the conversion is done. You can also edit the transcript yourself in our web editor later. If you are a developer, feel free to integrate our API. It's easy.

You do not have SpokenData.com account yet? Just register here and you can process your data in 1 minute!

Use case: How to transcribe conference video recordings and make subtitles for them?

One handy usage of automatic speech recognition technologies - speech-to-text - is a transcription of conference talks. There are plenty of conferences and lots of them are being recorded and published on a conference homepage or YouTube for example.
Let's use any conference as an example. To record the conference and to have plenty of videos on YouTube is fine, but it starts to be messy. You can find useful following reasons for transcribing talks.

1. Some people do not understand English very well. Reading subtitles can help them understand.
2. You need to market your conference to attract people. Videos show the quality of your conference to prospects. Transcribing the video to text increases your SEO. More people will find you.
3. Large collections of videos can be searchable with a difficulty for particular information. Time synchronous speech transcript can help you search in speech quickly even in a large collection of videos.

To use human labor for subtitling videos make sense, because people do not like watching subtitles with errors - and automatic voice to text can make errors. On the other hand, transcribing all recordings from a several day long conference can be enormously expensive on human resources.
So the use of automatic voice to text technology is a logical step to reduce the need of human resources. Especially for cases 2) and 3). Here you do not care about a few errors, because the transcript is primarily for machines - search engines.

The huge advantage of our service here is the ability of automatic speech recognizer adaptation on the target domain - your conference. Usually, every technical conference has proceedings which are full of content words, abbreviations, technical terms etc. These words are important (within you conference) but rare in general speech. So standard recognizers trained on general speech can miss them easily and the transcript is useless for you.

To give you a real use case, SuperLectures - a conference video service - uses SpokenData.com automatic transcriptions in the above mentioned way. They provide us with proceedings so that we could adapt our recognizer. Then we return them textual transcription of their audio/video data.

There is no audio/video limitation on spokendata free plan now.

We changed the way how we limit the processed data for free accounts. To get a free SpokenData account, register here.

We previously had a hard limit of processed data set to 15 minutes. All data you uploaded over this limit was trimmed and discarded. So if you uploaded 25 minutes of audio or video and wanted the automatic transcript for free, you got only 15 minutes long video/audio with the transcript in your dashboard later (after the processing finished).

Currently, we do not limit your data upload. We just limit the length of the transcript we provide you for free. So if you upload 25 minute long video/audio, you will find the whole (25 minute long) video/audio in your dashboard. The generated transcript has the length limit set to 15 minutes, so you will see only the first 15 minutes of transcript for your data. But if you want, you can easily create the rest of the transcript for free yourself - this was not possible in the previous version because the video was trimmed.

We hope you welcome this change.

..and stay tuned. More interesting things are coming soon..

What does the speaker segmentation technology

Speaker segmentation (diarization) is a speech technology allowing you to segment audio (or video) into particular speakers. What is it good for? You can more easily identify speaker turns in a dialog while making speech transcript.

Even if you do not directly need the speaker information, the speaker segmentation is very helpful for speech-to-text technology (STT). The STT technology contains unsupervised speaker adaptation module. This module takes parts of speech belongings to a particular speaker and adapts an acoustic model towards them. Adaptation of the model leads to more accurate speech transcript.

The adaptation - even if it is called speaker adaptation - adapts the system to the whole acoustic channel. It consists of speaker's voice characteristics, room acoustics (echo), microphone characteristics, environment noise, etc.

Speaker segmentation is theoretically independent on speaker, language and acoustic conditions. But - practically - it is dependent. The reason is, that it uses something called a universal background model (UBM). The UBM should model all speech, languages and acoustics of the world - theoretically. But you need to train it on some speaker labeled data - to learn how to distinguish among speakers. And it holds (as in other speech technologies) that the more far the data you process is from the training data, the worse accuracy you get.

New Czech recognizer online

We are happy to announce that our new Czech recognizer is online at spokendata.com. The novelty lies in the support of 16kHz audio data (so called wideband) and also in robustness for distant microphones.
In comparison to the previous Czech recognizer, which was aimed at telephone data (8kHz - so called narrowband), the new one achieves better accuracy for:

• Speech recorded with higher quality - 16kHz or more. Unless you capture a telephone call, you usually record the data up to 44kHz.
• Distant microphone recordings. A telephone call recording is usually labeled as a close-talk because the speaker's mouth is close to the microphone. The distant microphone is the opposite case. Here the speaker's mouth is far from the microphone. As an example it can be a voice recorder or a mobile phone lying on the table while recording a dialog.

Fell free to test the recognizer by uploading your data and enjoy the transcript.