--- source_path: "models/types/lvcsr.md" canonical_url: "https://doc.sensory.com/tnl/7.8/models/types/lvcsr/" --- # LVCSR _(TrulyNatural only)_ These recognizers use a phonetic acoustic model and an FST vocabulary decoder. They are suitable for small to medium vocabulary tasks, but not for unconstrained audio transcription. These models have [task-type](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#task-type)` == `[lvcsr](https://doc.sensory.com/tnl/7.8/api/setting-keys/values.md#lvcsr) and filenames that by convention match `lvcsr-*.snsr` You can create LVCSR recognizers with [VoiceHub](https://doc.sensory.com/tnl/7.8/reference/voicehub.md#voicehub) or by [specifying a grammar](https://doc.sensory.com/tnl/7.8/models/types/lvcsr.md#grammar-based-recognition) with build-capable[^1] model. LVCSR recognizers include support for decoding with statistical [language models], but Sensory does not distribute the tools used to create these[^2]. Language models can provide improved accuracy for constrained target domains. _For transcription type tasks, an STT model is a better fit._ The Sensory FST decoder supports hybrid models that contain both grammar-based and language model components. **Also see these related items:** [LVCSR models](https://doc.sensory.com/tnl/7.8/models/index.md#lvcsr-models) included in this distribution. [^1]: LVCSR models created by [VoiceHub](https://doc.sensory.com/tnl/7.8/reference/voicehub.md#voicehub) include build components only if the grammar references at least one user-defined class, such as `~dynamic-1`. If the grammar contains no unresolved classes VoiceHub removes the build components to reduce model files size and RAM use. [^2]: Contact your [sales representative](https://doc.sensory.com/tnl/7.8/contact.md#sales) if you would like to explore using a custom language model for your application. ## Operation ```mermaid flowchart TD start((start)) fetch[/samples from ->audio-pcm/] audio(^sample-count) process partial(^result-partial) intent(^nlu-intent) slot(^nlu-slot) result(^result) nlu{NLU
match?} start --> fetch fetch --> audio audio --> process process --> fetch process -->|hypothesis| partial partial --> fetch process -->|VAD endpoint
or STREAM_END| nlu nlu -->|yes| intent nlu -->|no| result intent --> slot slot --> result slot -->|more| intent result --> fetch ``` Recognition flow. 1. Read audio data from [->audio-pcm](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#-audio-pcm). 2. Invoke [^sample-count](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#sample-count-event). 3. Invoke [^result-partial](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#result-partial) with interim recognition hypotheses every [partial-result-interval](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#partial-result-interval) ms. 5. Continue processing until [STREAM_END](https://doc.sensory.com/tnl/7.8/api/inference.md#rc_stream_end) occurs on [->audio-pcm](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#-audio-pcm), one of the event handlers returns a code other than [OK](https://doc.sensory.com/tnl/7.8/api/inference.md#rc_ok), or an external [VAD](https://doc.sensory.com/tnl/7.8/api/setting-keys/values.md#vad) detects a speech endpoint. 6. If NLU is configured, invoke [^nlu-intent](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-intent) and [^nlu-slot](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-slot) for each top-level result that matches. 7. Invoke [^result](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#result) with the final recognition hypothesis. 8. Resume processing from step 1. **Note:** LVCSR recognizers do **not** produce a final recognition hypothesis until they run out of audio samples to process, or an external VAD detects a speech endpoint. With live audio you should use these with a VAD template such as [tpl-vad-lvcsr](https://doc.sensory.com/tnl/7.8/models/index.md#tpl-vad-lvcsr), [tpl-opt-spot-vad-lvcsr](https://doc.sensory.com/tnl/7.8/models/index.md#tpl-opt-spot-vad-lvcsr), or [tpl-spot-vad-lvcsr](https://doc.sensory.com/tnl/7.8/models/index.md#tpl-spot-vad-lvcsr). ## Settings **Available events:** [^nlu-intent](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-intent), [^nlu-slot](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-slot), [^result](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#result), [^result-partial](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#result-partial), [^sample-count](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#sample-count-event) **Available iterators:** _none_ **Available results:** [audio-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#audio-stream), [audio-stream-first](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#audio-stream-first), [audio-stream-last](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#audio-stream-last) **Available runtime settings:** [->audio-pcm](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#-audio-pcm), [audio-stream-from](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#audio-stream-from), [audio-stream-to](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#audio-stream-to), [grammar-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#grammar-stream), [phrases-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#phrases-stream) **Available configuration settings:** [ac-prune-top-k](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#ac-prune-top-k), [audio-stream-size](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#audio-stream-size), [complete-only](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#complete-only), [partial-result-interval](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#partial-result-interval), [ram-limit](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#ram-limit), [samples-per-second](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#samples-per-second), [search.frame-nota](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#searchframe-nota), [show-silence](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#show-silence) **Available values:** [lvcsr](https://doc.sensory.com/tnl/7.8/api/setting-keys/values.md#lvcsr) **Also see these related items:** [live-spot.c](https://doc.sensory.com/tnl/7.8/api/sample/c/live-spot.md#live-spot-code), [snsr-eval.c](https://doc.sensory.com/tnl/7.8/api/sample/c/snsr-eval.md#snsr-eval-code), [PhraseSpot.java](https://doc.sensory.com/tnl/7.8/api/sample/android/enroll-trigger.md#et-code), [segmentSpottedAudio.java](https://doc.sensory.com/tnl/7.8/api/sample/java/segmentSpottedAudio.md#segmentspottedaudio-code) ## Notes Sensory optimizes hybrid models with a background component only to detect speech that is not in the specified grammar. These models report an [nlu-intent-name](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#nlu-intent-name) of `background` when they detect out-of-grammar utterances. You should not use the out-of-grammar recognition [text](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#text) result as this will have a high word error rate. Consider using [STT](https://doc.sensory.com/tnl/7.8/models/types/stt.md#stt-type) for transcription tasks instead. [phrases-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#phrases-stream) provides a convenient way to specify a recognition vocabulary from an exhaustive list of alternative utterances. ## Grammar-based recognition Sensory's LVCSR models use [grammars](https://doc.sensory.com/tnl/7.8/models/types/lvcsr.md#grammar-syntax) to constrain the possible utterances they can recognize. Focussing on a limited set of words and structures defined in these grammars improves recognition speed and accuracy at the expense of recognizing arbitrary input. You can create a custom recognizer by specifying a fixed grammar during development if the recognition vocabulary is entirely known, or at runtime if it is not. You can also use a hybrid approach and build the invariant parts during development, and delay adding [variable parts](https://doc.sensory.com/tnl/7.8/models/types/lvcsr.md#grammar-classes) (such as a list of favorite TV channels) until runtime. ### Creating a recognizer Create a grammar-based recognizer using the [command-line tools](https://doc.sensory.com/tnl/7.8/tools/index.md#command-line-tools). This example uses _data/grammars/enrollments.txt_ which contains a sample grammar specification for the enrollment recordings in _data/enrollments/_. To create a custom recognizer using this grammar with [snsr-edit](https://doc.sensory.com/tnl/7.8/tools/snsr-edit.md#snsr-edit), specify an LVCSR model that supports building and [grammar-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#grammar-stream). ### Details: _data/grammars/enrollments.txt_ ``` # LVCSR grammar specification for test utterances in data/enrollments/ # # In a tpl-spot-vad-lvcsr pipeline the prefix would be consumed by the spotter. prefix = armadillo | jackalope | terminator; # List of known utterances in the *-c.wav files. sentence = 18 percent of 643 | call the nearest target | how far away is winco | play more songs by this artist | record a video | start a timer for 20 minutes | i'm running low on gas | cancel all my meetings on friday | directions to susan's house | do i have any new texts | open my calendar to next week | set an alarm for 6 am tomorrow; # Match the prefix and zero or one of the sentences. # and are sentence start and end markers that # match silence and small amounts of extraneous speech. g = $prefix $sentence? ; ``` ```console % cd $HOME/Sensory/TrulyNaturalSDK/7.9.0-pre.0 % bin/snsr-edit -vv -t model/lvcsr-build-enUS-14.0.2-5MB.snsr \ -f grammar-stream data/grammars/enrollments.txt \ -o lvcsr-enrollments.snsr Loading "model/lvcsr-build-enUS-14.0.2-5MB.snsr" as the template model. Loading "data/grammars/enrollments.txt" into setting "grammar-stream". Saved edited model to "lvcsr-enrollments.snsr". ``` Run the new model with [snsr-eval](https://doc.sensory.com/tnl/7.8/tools/snsr-eval.md#snsr-eval): ```console % bin/snsr-eval -t lvcsr-enrollments.snsr \ -s partial-result-interval=0 \ # (1)! data/enrollments/armadillo-1-3-c.wav 165 2745 armadillo play more songs by this artist ``` 1. [partial-result-interval](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#partial-result-interval)` = 0` shows only the final recognition hypothesis. For small grammars such as this the build time is negligible. [snsr-eval](https://doc.sensory.com/tnl/7.8/tools/snsr-eval.md#snsr-eval) can build and run the recognizer in a single operation: ```console % bin/snsr-eval -t model/lvcsr-build-enUS-14.0.2-5MB.snsr \ -f grammar-stream data/grammars/enrollments.txt \ -s partial-result-interval=0 \ data/enrollments/armadillo-1-3-c.wav 165 2745 armadillo play more songs by this artist ``` ### Classes A symbol that starts with the tilde `~` sigil specifies a [recognition class](https://doc.sensory.com/tnl/7.8/models/types/lvcsr.md#grammar-syntax-class). Class recognizers have their own grammar specifications, separate from the top-level grammar. The behavior of a class-based recognizer is similar to that specified by a rule. Classes, however, can be updated without recompiling the rest of the grammar, and all references to a class use the same recognizer. This can reduce the recognizer size and improve build speed. This example uses a modified enrollment grammar which references two toy classes: `~number` and `~place`: **`enrollments-class.txt`** ``` # LVCSR grammar specification for test utterances in data/enrollments/ # This references two class sub-recognizers: ~number and ~place # # In a tpl-spot-vad-lvcsr pipeline the prefix would be consumed by the spotter. prefix = armadillo | jackalope | terminator; # List of known utterances in the *-c.wav files. sentence = ~number percent of ~number | call the nearest ~place | how far away is ~place | play more songs by this artist | record a video | start a timer for ~number minutes | i'm running low on gas | cancel all my meetings on friday | directions to ~place | do i have any new texts | open my calendar to next week | set an alarm for ~number am tomorrow; # Match the prefix and zero or one of the sentences. # and are sentence start and end markers that # match silence and small amounts of extraneous speech. g = $prefix $sentence? ; ``` **`place.txt`** ``` # Example place name class recognizer. g = target | winco | susan's house; ``` The `~number` and `~place` classes referenced in _enrollments-class.txt_ create two new dynamic settings for these classes: `grammar-stream.number` and `grammar-stream.place`. Specify these to create a complete recognizer: ```console % snsr-edit -v -t model/lvcsr-build-enUS-14.0.2-5MB.snsr\ -f grammar-stream enrollments-class.txt \ -g grammar-stream.number "g = 18 | 643 | 20 | 6;" \ # (1)! -o lvcsr-enrollments-class.snsr Output written to "lvcsr-enrollments-class.snsr". ``` 1. [snsr-edit](https://doc.sensory.com/tnl/7.8/tools/snsr-edit.md#snsr-edit)'s `-g` option sets the [grammar-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#grammar-stream)`.number` stream to a string argument. A file can also be used for the number grammar. Run the recognizer: ```console % snsr-eval -v -t lvcsr-enrollments-class.snsr \ -s partial-result-interval=0 \ data/enrollments/armadillo-1-0-c.wav 375 3150 (1.863e-08) armadillo 18 percent of 643 ``` ### Class libraries TrulyNatural 6.15.0 introduced support for pre-built binary class repositories. These contain classes built from frequently used grammar fragments such as dates, times, and numbers. Load binary class repositories into the same [Session](https://doc.sensory.com/tnl/7.8/api/inference.md#session) as an LVCSR model to add this capability to the model. If a grammar references a class that's not explicitly defined, the class name is looked up in the provided class library or libraries. System class libraries provided by Sensory use a prefix of `s.` for all class names. See [lvcsr-lib-enUS-14.0.2.snsr](https://doc.sensory.com/tnl/7.8/models/index.md#lvcsr-lib-enUS) for a description of the classes used below. **`class-lib.txt`** ``` # Example recognizer with classes from a class library call = call {number ~s.phone-number}; emergency = ~s.call-emergency; timer = {timer ~s.timer-phrases}; commands = {call} | {emergency} | $timer; g = $commands ; ``` This example uses live audio, so it needs [snsr-eval](https://doc.sensory.com/tnl/7.8/tools/snsr-eval.md#snsr-eval)'s `-a` flag to add a [VAD](https://doc.sensory.com/tnl/7.8/models/tpl/tpl-vad-lvcsr.md#tpl-vad-lvcsr-type) to find the end of each utterance and signal the recognizer to produce a final hypothesis. ```console % snsr-eval -a -t model/lvcsr-build-enUS-14.0.2-5MB.snsr \ -t model/lvcsr-lib-enUS-14.0.2.snsr \ -f grammar-stream class-lib.txt \ -s partial-result-interval=0 # Say: Call 1 800 555 1212 NLU intent: call (0) = call one eight hundred five five five one two one two NLU entity: number (0) = one eight hundred five five five one two one two 3360 6855 call one eight hundred five five five one two one two # Say: Set a timer for 31 minutes. NLU intent: timer (0) = set a timer for thirty one minutes 14610 16770 set a timer for thirty one minutes # Say: Call the fire department. NLU intent: emergency (0) = call the fire department 24540 25890 call the fire department ``` **C/C++** Configuring class-based recognition with the C API: ```c SnsrSession s; snsrNew(&s); snsrLoad(s, snsrStreamFromFileName("model/tpl-vad-lvcsr-3.17.0.snsr", "r")); snsrSetStream(s, SNSR_SLOT_0, snsrStreamFromFileName("model/lvcsr-build-enUS-14.0.2-5MB.snsr", "r")); snsrLoad(s, snsrStreamFromFileName("model/lvcsr-lib-enUS-14.0.2.snsr", "r")); snsrSetStream(s, SNSR_GRAMMAR_STREAM, snsrStreamFromFileName("class-lib.txt", "r")); if (snsrRC(s) != SNSR_RC_OK) { fprintf(stderr, "ERROR: %s\n", snsrErrorDetail(s)); return snsrRC(s); } ``` **Java** Configuring class-based recognition with the Java API: ```java SnsrSession s = new SnsrSession(); try { s.load(SnsrStream.fromFileName("model/tpl-vad-lvcsr-3.17.0.snsr", "r")); s.setStream(Snsr.SLOT_0, SnsrStream.fromFileName("model/lvcsr-build-enUS-14.0.2-5MB.snsr", "r")); s.load(SnsrStream.fromFileName("model/lvcsr-lib-enUS-14.0.2.snsr", "r")); s.setStream(Snsr.GRAMMAR_STREAM, SnsrStream.fromFileName("class-lib.txt", "r")); } catch (IOException e) { e.printStackTrace(); return s.rC(); } ``` **Python** Configuring class-based recognition with the Python API: ```python try: with snsr.Session() as s: s.load("model/tpl-vad-lvcsr-3.17.0.snsr") s.set_stream( snsr.SLOT_0, snsr.Stream.from_filename("model/lvcsr-build-enUS-14.0.2-5MB.snsr", "r"), ) s.load("model/lvcsr-lib-enUS-14.0.2.snsr") s.set_stream( snsr.GRAMMAR_STREAM, snsr.Stream.from_filename("class-lib.txt", "r"), ) except snsr.Error as e: print(f"ERROR: {e.message}") ``` ### Syntax A [context-free grammar] is a set of rules that describes the sequences of words that an LVCSR model can recognize. #### Definition 1. Grammars use [UTF-8][] encoding. 1. `#` marks the start of a comment, which extends to the end of the line. 1. A _grammar_ is a series of _rules_ representing variable definitions. The final rule in a grammar specifies the recognition vocabulary and typically references rules defined earlier. It should include the sentence start (``) and end (``) markers. 1. A _rule_ is an assignment of the form `name = expr ;` where `name` is a _symbol_ and `expr` is a sequence of _symbols_ and _operators_. `expr` is a type of [regular expression][]. 1. A _symbol_ is a sequence of characters that does not include any whitespace or operators, optionally prefixed by sigils `$` or `~`. A symbol without a sigil is called a _terminal_ and is part of the recognition vocabulary, for example `temperature`. [Special symbols](https://doc.sensory.com/tnl/7.8/models/types/lvcsr.md#grammar-special) are predefined terminals that describe input characteristics such as pauses and the edges of an utterance. 1. The `$` sigil does rule substitution _at build time_. The parser substitutes the value of the rule named `name` for `$name`. Substitutions include an implicit _grouping_ operator: Grammar `a = 1 | 2 | 3; b = $a ;` is equivalent to `b = (1 | 2 | 3) ;`. 1. The `~` sigil substitutes a named recognition class _at runtime_. - Each class is a recognizer with its own grammar, separate from the main grammar. - All references to a class use instances of the same class recognizer. - You can update each class in isolation, without having to recompile the main grammar. - If you have a large rule that's referenced multiple times, converting it to a class can speed up build time significantly. - Use classes to augment a recognition vocabulary at runtime. In a voice dialing application, for example, one would define the entire recognition grammar at build time but use `~contacts` instead of a predefined list of contact names. Once loaded, the application would scan the address book and build only the `~contacts` class. - Specify class definitions with [grammar-stream.classname](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#grammar-stream) or [phrases-stream.classname](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#phrases-stream), for example `phrases-stream.contacts`. 2. Operators include _grouping_ parentheses, brackets, and braces, _infix_ operators that indicate logical AND and OR between symbols, and _postfix_ operators that change how the preceding symbol matches input. The [operator precedence](https://doc.sensory.com/tnl/7.8/models/types/lvcsr.md#grammar-op-precedence) table lists the order and direction in which the parser applies operators. 3. Grouping - `( )` Parentheses enclose items that are grouped together. - `[ ]` Square brackets enclose optional items. `[...]` is equivalent to `(...)?`. - `{ }` Braces implement slot-capturing lightweight NLU markup. - `{slotName a b c}` makes `a b c` available as the [nlu-slot-value](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#nlu-slot-value) of [nlu-slot-name](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#nlu-slot-name) `slotName` when the recognizer matches `a b c` to the input audio. - You can nest NLU slots to an arbitrary depth. - The outermost slots are defined as [intents](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#nlu-intent-name) and all the nested slots in each intent as [entities](https://doc.sensory.com/tnl/7.8/api/setting-keys/results.md#nlu-entity-name). - Each identified intent invokes handlers registered for [^nlu-intent](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-intent) and [^nlu-slot](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-slot). - `{rule}` is shorthand for `{rule $rule}`. - With this grammar: ``` seconds = 1 | 2 | 4 | 8 | half:0.5 a:? | a:? quarter:0.25 [of: a:]; shutterSpeed = set shutter speed to {seconds} ( second | seconds ); cmd = {shutterSpeed} ; ``` an utterance of "set shutter speed to a quarter of a second" will produce `set shutter speed to 0.25 second` as recognition output, with an additional [^nlu-intent](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-intent) callback for the top-level `shutterSpeed` slot: ``` NLU intent: shutterSpeed (0) = set shutter speed to 0.25 second NLU entity: seconds (0) = 0.25 ``` 4. Infix operators - These are valid between symbols and may be surrounded by whitespace. - `^` is the conjunction operator and is implied between adjacent terminals: Grammar `g = one two three;` will recognize only the sequence "one two three". - `|` is the disjunctive operator. It separates alternative items. Grammar `g = one | two | three;` will recognize "one", or "two", or "three". 5. Postfix operators - These directly follow a symbol without any intervening whitespace. - `?` A question mark following a symbol makes that symbol optional: It requires zero or one repetitions of the symbol. - `+` A plus sign following a symbol or a group requires one or more repetitions of it. - `*` An asterisk following a symbol or a group requires zero or more repetitions. - `:` is the rewrite operator. - `left:right` recognizes symbol `left` but produces terminal `right` as a recognition result. - `left:` recognizes symbol `left` but rewrites that to an empty string, eliding `left` from the recognition result. - `:right` inserts `right` into the recognition result. If you say "one two three", grammar `g = one :mississippi two :mississippi three ;` produces "one mississippi two mississippi three". - `/` A forward slash following a symbol followed by a floating point number defines a weight to be associated with that symbol. If there's a rewrite operator (`:`) the slash must follow the rewritten-to terminal, for example: `one:een/0.123` Weights are in the logprob domain, convert from a $[0, 1]$ probability to a weight with $w = -log_{10}(p)$. The default symbol weight is `0` for a probability of `1.0`. 6. `\` escape symbol. To include a literal special character in a grammar specification, escape it with a backslash. The list of characters that support this include: `^`, `|`, `*`, `+`, `?`, `=`, `[ ]`, `( )`, `;`, `#`, and `:`. **Also see these related items:** [grammar-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#grammar-stream), [phrases-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#phrases-stream), [nlu-grammar-stream](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#nlu-grammar-stream), [^nlu-intent](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-intent), [^nlu-slot](https://doc.sensory.com/tnl/7.8/api/setting-keys/events.md#nlu-slot) #### Operator precedence The following table lists the precedence and associativity of grammar operators. Operators are listed in descending precedence: level `0` is applied first and level `5` last. Precedence | Operator | Description | Associativity :---------:|:--------:|-------------|-------------- 0 | `:` | Rewrite output 0 | `/` | Symbol weight 1 | `( )` | Grouping 1 | `[ ]` | Optional group 1 | `{ }` | Slot-capturing semantic markup 2 | `?` | Zero-or-one symbol | left-to-right 2 | `+` | One-or-more symbols | left-to-right 2 | `*` | Zero-or-more symbols | left-to-right 3 | `^` | And, implied between symbols | right-to-left 4 | `|` | Alternative | right-to-left 5 | `=` | Rule assignment | right-to-left This grammar: ``` a = one | two three four; g = ( $a | five six) ; ``` will recognize only these phrases: ``` one two three four five six ``` #### Special symbols A grammar can include these special symbols: - `` - The silence at the start of a sentence. - `` - The silence at the end of a sentence. - `` - Short pauses between words. The grammar compiler automatically adds these where needed, so there is no need to do so explicitly. Do **not** add `` to NLU grammars, use `` instead. - `` - A explicit short pause. - `` - Matches when none of the alternatives are likely (i.e. "none of the above"). + Recognition results at the phrase level can include `` even if this symbol was not explicitly used in the grammar. This is an indication that the result was rejected due to [search.frame-nota](https://doc.sensory.com/tnl/7.8/api/setting-keys/configuration.md#searchframe-nota), or that RAM or CPU constraints limited the recognizer's ability to produce a result. - `` - Similar to ``. In *some* models the threshold for determining whether this symbol matches better than any other is different from that of ``. - `.` - When used with lightweight [NLU grammars](https://doc.sensory.com/tnl/7.8/api/setting-keys/runtime.md#nlu-grammar-stream) a single period matches any input word. Use `.:*` to match any input words and remove them from the NLU result. [regular expression]: https://en.wikipedia.org/wiki/Regular_expression [UTF-8]: https://en.wikipedia.org/wiki/UTF-8 *[API]: Application Programming Interface *[FST]: Finite-State Transducer *[LVCSR]: Large Vocabulary Continuous Speech Recognition model, feed-forward neural net acoustic model with FST decoder *[NLU]: Natural Language Understanding model *[RAM]: Random Access Memory *[STT]: Speech To Text: transformers with language model and CTC decoding *[TNL]: TrulyNatural, Sensory's large-vocabulary speech recognition technology *[VAD]: Voice Activity Detector