![]() There are also various processing metric features which can be retrieved.This feature disables keyword spotting, interim results as well as alternative transcripts Service can provide multiple word alternatives for an unclear utterance or can also provide multiple final transcripts with possible alternativesĪvailable for US English, Japanese, and SpanishConverts following things into more conventional formatsDatesTimesSeries of digits and numbersPhone numbersCurrency valuesInternet email and web addressesĪvailable for US English, Japanese, and SpanishRedaction, removal of numbers, sensitive numerical data. Word alternatives and alternative transcripts ![]() Probability threshold enables inclusion of keywords with increasing certainty Up to 1000 keywords can be spotted in final transcript. Keyword spotting with probability thresholds Provides interim transcription as audio progresses Such transcription tends to change in final transcript Service generally takes 1 minute to stabilize and provide more accurate output. Longer conversations are better detected than shorter utterances. Optimized for 2 speakers, can detect up to 6 speakers. That means contextual information is retained. Even for streaming audio, results are only outputted for complete conversation i.e. IBM Watson Speech-To-Text works for audio files as well as streaming data.Not much information has been revealed about which different sentiments can be detected.Ĭustomization of pretrained models (using transfer learning) with custom vocabulary or other similar tools does not seem to be possible or at least not well documented. Output features available are sentiment analysis, entity analysis, entity sentiment analysis, syntactic analysis and content classification.Currently supported languages are English, Spanish, Japanese, Chinese (simplified and traditional), French, German, Italian, Korean, Portuguese, and Russian.Pretrained models offer all standard features like syntax detection, entity extraction etc. Even though AutoML way might look enticing, it has one of the biggest hurdles that models must be trained using custom data and therefore, may not be an easy task. It offers two avenues – custom processing models using Google AutoML or pretrained models. The NL processing and understanding offering of GCP is interesting.Google offers to forbid data logging for privacy purposes.For all other languages, default trained model must be used. It also costs more than the default recognition model. A special “phone_call” machine learning model exists for transcribing speech from telephonic conversations, but it only currently supports English US language.gRPC is global remote procedure call meant to simplify accessing and calling remote functions on any server. Streaming support with gRPC bi-directional stream.5 KB is the maximum size of the document that is accepted for many types of analysis (e.g.Custom entities and classification rules can be externally supplied with an AutoML model.Amazon Comprehend provides Key-phrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs.What limits are applied to streaming data is unclear. For example, only 100 concurrent transcription jobs are supported, or maximum size of custom vocabulary can only be 50 KB. But there are overall limits on service usage. AWS seems to support a huge amount of audio data files for transcription.AWS documents various security techniques but does not mention whether the voice data will be used in enhancement of their own algorithms or not.Custom vocabulary is supported but entries are constrained to 256 characters including separators like hyphen.This can be used for transcribing calls or multichannel audio conversation. Transcribe provides channel identification in voice data.Text to speech service, can use audio files as well as voice streams (using WebSockets).While listing features, attention has been paid to evaluate how many different characteristics the service provides, how easy it is to use the API and which interfaces (REST, WebSockets etc.) are available, how confidentially the data will be handled (important for GDPR!) and which natural languages are supported. Amazon, Google, IBM and Microsoft, and then provide a brief comparison. We will first list the noteworthy features of services of big 4 cloud providers – viz. In order to do so, speech must first be converted to text and then later analyzed. But considering that a lot of day-to-day interactions are vocal, and a lot of data can be extracted from these interactions. One might wonder why particularly these two branches. In this post we will venture into detailed comparison of Speech-To-Text (STT) and Natural Language Understanding (NLU) services portfolio. In last post we took an overview of ready to use API services for various AI branches.
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