Patent Document ID: 9484023
Application ID: 13773880

Base Claim:
1. A method, comprising: converting a non-back-off language model to a back-off language model using a background language model, wherein the non-back-off language model assigns a probability to any fixed order n-gram without backing off to lower order n-gram probabilities, and wherein the background language model assigns a non-zero probability to n-grams assigned a zero probability by the non-back-off language model; and pruning the converted back-off language model; wherein the converted back-off language model assigns a probability to a given fixed order n-gram assigned a zero probability by the non-back-off language model and not assigned a fixed order probability by the background language model by backing off to a given lower order n-gram corresponding to the given fixed order n-gram; wherein the given fixed order n-gram comprises a given word and an associated history; wherein the given lower order n-gram comprises the given word and a truncated version of the associated history; wherein lower order n-grams originate from at least one lower order non-back-off language model after converting the lower order non-back-off language model to the back-off language model and highest order n-grams originate from an n-gram language model; and wherein the converted back-off language model is usable for decoding speech, and the converting and pruning steps are executed via a processor device configured to implement at least one speech decoder associated with an automatic speech recognition system configured to integrate the converted back-off language model into a decoding process.

---

Claim 17:
17. The method of claim 1 , wherein the converted back-off language model is given by: P ⁡ ( w | h ) = ⁢ β ⁡ ( h ) ⁢ P NNLM ⁡ ( w | h ) ⁢ if ⁢ w ∈ V o ⁢ P BLM ⁡ ( w | h ) ⁢ if ⁢ w ∉ V o ⁢ ⁢ w ⁢ : ⁢ ⁢ ⁢ B ⁢ ⁢ O ⁡ ( w , h ) ⁢ α ⁡ ( h ) ⁢ P BLM ⁡ ( w | h ′ ) ⁢ if ⁢ w ∉ V o ⁢ ⁢ w ⁢ : ⁢ ⁢ B ⁢ ⁢ O ⁡ ( w , h ) where P(w|h) denotes the probability assigned to a fixed order n-gram comprising word w and an associated history h, β(h) denotes a normalization constant for history h, P NNLM (w|h) denotes the probability assigned to the fixed order n-gram by the non-back-off language model, V o denotes an output vocabulary of the non-back-off language model, P BLM (w|h) denotes the fixed order probability assigned by the back-off language model, α(h) denotes a back-off weight associated with history h, h′ denotes a truncated version of history h, P BLM (w|h′) denotes the probability assigned by backing off to the lower-order n-gram corresponding to the fixed order n-gram, w: BO(w,h) denotes that the fixed order n-gram is assigned a probability by the background language model, and w:BO(w,h) denotes that the fixed order n-gram is not assigned a probability by the background language model.